diff --git a/solution/3600-3699/3617.Find Students with Study Spiral Pattern/README.md b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/README.md
new file mode 100644
index 0000000000000..9ece3baa11e91
--- /dev/null
+++ b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/README.md	
@@ -0,0 +1,427 @@
+---
+comments: true
+difficulty: 困难
+edit_url: https://github.com/doocs/leetcode/edit/main/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README.md
+tags:
+    - 数据库
+---
+
+<!-- problem:start -->
+
+# [3617. Find Students with Study Spiral Pattern](https://leetcode.cn/problems/find-students-with-study-spiral-pattern)
+
+[English Version](/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README_EN.md)
+
+## 题目描述
+
+<!-- description:start -->
+
+<p>Table: <code>students</code></p>
+
+<pre>
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| student_id   | int     |
+| student_name | varchar |
+| major        | varchar |
++--------------+---------+
+student_id is the unique identifier for this table.
+Each row contains information about a student and their academic major.
+</pre>
+
+<p>Table: <code>study_sessions</code></p>
+
+<pre>
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| session_id    | int     |
+| student_id    | int     |
+| subject       | varchar |
+| session_date  | date    |
+| hours_studied | decimal |
++---------------+---------+
+session_id is the unique identifier for this table.
+Each row represents a study session by a student for a specific subject.
+</pre>
+
+<p>Write a solution to find students who follow the <strong>Study Spiral Pattern</strong>&nbsp;- students who consistently study multiple subjects in a rotating cycle.</p>
+
+<ul>
+	<li>A Study Spiral Pattern means a student studies at least <code>3</code><strong> different subjects</strong> in a repeating sequence</li>
+	<li>The pattern must repeat for <strong>at least </strong><code>2</code><strong> complete cycles</strong> (minimum <code>6</code> study sessions)</li>
+	<li>Sessions must be <strong>consecutive dates</strong> with no gaps longer than <code>2</code> days between sessions</li>
+	<li>Calculate the <strong>cycle length</strong> (number of different subjects in the pattern)</li>
+	<li>Calculate the <strong>total study hours</strong> across all sessions in the pattern</li>
+	<li>Only include students with cycle length of <strong>at least </strong><code>3</code><strong> subjects</strong></li>
+</ul>
+
+<p>Return <em>the result table ordered by cycle length in <strong>descending</strong> order, then by total study hours in <strong>descending</strong> order</em>.</p>
+
+<p>The result format is in the following example.</p>
+
+<p>&nbsp;</p>
+<p><strong class="example">Example:</strong></p>
+
+<div class="example-block">
+<p><strong>Input:</strong></p>
+
+<p>students table:</p>
+
+<pre class="example-io">
++------------+--------------+------------------+
+| student_id | student_name | major            |
++------------+--------------+------------------+
+| 1          | Alice Chen   | Computer Science |
+| 2          | Bob Johnson  | Mathematics      |
+| 3          | Carol Davis  | Physics          |
+| 4          | David Wilson | Chemistry        |
+| 5          | Emma Brown   | Biology          |
++------------+--------------+------------------+
+</pre>
+
+<p>study_sessions table:</p>
+
+<pre class="example-io">
++------------+------------+------------+--------------+---------------+
+| session_id | student_id | subject    | session_date | hours_studied |
++------------+------------+------------+--------------+---------------+
+| 1          | 1          | Math       | 2023-10-01   | 2.5           |
+| 2          | 1          | Physics    | 2023-10-02   | 3.0           |
+| 3          | 1          | Chemistry  | 2023-10-03   | 2.0           |
+| 4          | 1          | Math       | 2023-10-04   | 2.5           |
+| 5          | 1          | Physics    | 2023-10-05   | 3.0           |
+| 6          | 1          | Chemistry  | 2023-10-06   | 2.0           |
+| 7          | 2          | Algebra    | 2023-10-01   | 4.0           |
+| 8          | 2          | Calculus   | 2023-10-02   | 3.5           |
+| 9          | 2          | Statistics | 2023-10-03   | 2.5           |
+| 10         | 2          | Geometry   | 2023-10-04   | 3.0           |
+| 11         | 2          | Algebra    | 2023-10-05   | 4.0           |
+| 12         | 2          | Calculus   | 2023-10-06   | 3.5           |
+| 13         | 2          | Statistics | 2023-10-07   | 2.5           |
+| 14         | 2          | Geometry   | 2023-10-08   | 3.0           |
+| 15         | 3          | Biology    | 2023-10-01   | 2.0           |
+| 16         | 3          | Chemistry  | 2023-10-02   | 2.5           |
+| 17         | 3          | Biology    | 2023-10-03   | 2.0           |
+| 18         | 3          | Chemistry  | 2023-10-04   | 2.5           |
+| 19         | 4          | Organic    | 2023-10-01   | 3.0           |
+| 20         | 4          | Physical   | 2023-10-05   | 2.5           |
++------------+------------+------------+--------------+---------------+
+</pre>
+
+<p><strong>Output:</strong></p>
+
+<pre class="example-io">
++------------+--------------+------------------+--------------+-------------------+
+| student_id | student_name | major            | cycle_length | total_study_hours |
++------------+--------------+------------------+--------------+-------------------+
+| 2          | Bob Johnson  | Mathematics      | 4            | 26.0              |
+| 1          | Alice Chen   | Computer Science | 3            | 15.0              |
++------------+--------------+------------------+--------------+-------------------+
+</pre>
+
+<p><strong>Explanation:</strong></p>
+
+<ul>
+	<li><strong>Alice Chen (student_id = 1):</strong>
+
+    <ul>
+    	<li>Study sequence: Math &rarr; Physics &rarr; Chemistry &rarr; Math &rarr; Physics &rarr; Chemistry</li>
+    	<li>Pattern: 3 subjects (Math, Physics, Chemistry) repeating for 2 complete cycles</li>
+    	<li>Consecutive dates: Oct 1-6 with no gaps &gt; 2 days</li>
+    	<li>Cycle length: 3 subjects</li>
+    	<li>Total hours: 2.5 + 3.0 + 2.0 + 2.5 + 3.0 + 2.0 = 15.0 hours</li>
+    </ul>
+    </li>
+    <li><strong>Bob Johnson (student_id = 2):</strong>
+    <ul>
+    	<li>Study sequence: Algebra &rarr; Calculus &rarr; Statistics &rarr; Geometry &rarr; Algebra &rarr; Calculus &rarr; Statistics &rarr; Geometry</li>
+    	<li>Pattern: 4 subjects (Algebra, Calculus, Statistics, Geometry) repeating for 2 complete cycles</li>
+    	<li>Consecutive dates: Oct 1-8 with no gaps &gt; 2 days</li>
+    	<li>Cycle length: 4 subjects</li>
+    	<li>Total hours: 4.0 + 3.5 + 2.5 + 3.0 + 4.0 + 3.5 + 2.5 + 3.0 = 26.0&nbsp;hours</li>
+    </ul>
+    </li>
+    <li><strong>Students not included:</strong>
+    <ul>
+    	<li>Carol Davis (student_id = 3): Only 2 subjects (Biology, Chemistry) - doesn&#39;t meet minimum 3 subjects requirement</li>
+    	<li>David Wilson (student_id = 4): Only 2 study sessions with a 4-day gap - doesn&#39;t meet consecutive dates requirement</li>
+    	<li>Emma Brown (student_id = 5): No study sessions recorded</li>
+    </ul>
+    </li>
+
+</ul>
+
+<p>The result table is ordered by cycle_length in descending order, then by total_study_hours in descending order.</p>
+</div>
+
+<!-- description:end -->
+
+## 解法
+
+<!-- solution:start -->
+
+### 方法一
+
+<!-- tabs:start -->
+
+#### MySQL
+
+```sql
+# Write your MySQL query statement below
+WITH
+    -- 第一步：为每个学生的学习记录按照日期排序并编号
+    ranked_sessions AS (
+        SELECT
+            s.student_id,
+            ss.session_date,
+            ss.subject,
+            ss.hours_studied,
+            ROW_NUMBER() OVER (
+                PARTITION BY s.student_id
+                ORDER BY ss.session_date
+            ) AS rn
+        FROM
+            study_sessions ss
+            JOIN students s ON s.student_id = ss.student_id
+    ),
+    -- 第二步：计算当前学习日期与前一次学习的日期差
+    grouped_sessions AS (
+        SELECT
+            *,
+            DATEDIFF(
+                session_date,
+                LAG(session_date) OVER (
+                    PARTITION BY student_id
+                    ORDER BY session_date
+                )
+            ) AS date_diff
+        FROM ranked_sessions
+    ),
+    -- 第三步：将学习记录按照日期差是否大于2进行分组（连续段）
+    session_groups AS (
+        SELECT
+            *,
+            SUM(
+                CASE
+                    WHEN date_diff > 2
+                    OR date_diff IS NULL THEN 1
+                    ELSE 0
+                END
+            ) OVER (
+                PARTITION BY student_id
+                ORDER BY session_date
+            ) AS group_id
+        FROM grouped_sessions
+    ),
+    -- 第四步：筛选出每个学生的每个连续学习段中包含至少6次学习的序列
+    valid_sequences AS (
+        SELECT
+            student_id,
+            group_id,
+            COUNT(*) AS session_count,
+            GROUP_CONCAT(subject ORDER BY session_date) AS subject_sequence,
+            SUM(hours_studied) AS total_hours
+        FROM session_groups
+        GROUP BY student_id, group_id
+        HAVING session_count >= 6
+    ),
+    -- 第五步：检测是否存在重复的科目循环模式
+    pattern_detected AS (
+        SELECT
+            vs.student_id,
+            vs.total_hours,
+            vs.subject_sequence,
+            COUNT(
+                DISTINCT
+                SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', n), ',', -1)
+            ) AS cycle_length
+        FROM
+            valid_sequences vs
+            JOIN (
+                -- 生成1到100的数字，用于提取第n个科目
+                SELECT a.N + b.N * 10 + 1 AS n
+                FROM
+                    (
+                        SELECT 0 AS N
+                        UNION
+                        SELECT 1
+                        UNION
+                        SELECT 2
+                        UNION
+                        SELECT 3
+                        UNION
+                        SELECT 4
+                        UNION
+                        SELECT 5
+                        UNION
+                        SELECT 6
+                        UNION
+                        SELECT 7
+                        UNION
+                        SELECT 8
+                        UNION
+                        SELECT 9
+                    ) a,
+                    (
+                        SELECT 0 AS N
+                        UNION
+                        SELECT 1
+                        UNION
+                        SELECT 2
+                        UNION
+                        SELECT 3
+                        UNION
+                        SELECT 4
+                        UNION
+                        SELECT 5
+                        UNION
+                        SELECT 6
+                        UNION
+                        SELECT 7
+                        UNION
+                        SELECT 8
+                        UNION
+                        SELECT 9
+                    ) b
+            ) nums
+                ON n <= 10
+        WHERE
+            -- 简化匹配：检查前半段和后半段是否相同（即是否重复）
+            LENGTH(subject_sequence) > 0
+            AND LOCATE(',', subject_sequence) > 0
+            AND (
+                -- 匹配3科循环2轮的模式
+                subject_sequence LIKE CONCAT(
+                    SUBSTRING_INDEX(subject_sequence, ',', 3),
+                    ',',
+                    SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', 6), ',', -3),
+                    '%'
+                )
+                OR subject_sequence LIKE CONCAT( -- 匹配4科循环2轮的模式
+                    SUBSTRING_INDEX(subject_sequence, ',', 4),
+                    ',',
+                    SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', 8), ',', -4),
+                    '%'
+                )
+            )
+        GROUP BY vs.student_id, vs.total_hours, vs.subject_sequence
+    ),
+    -- 第六步：拼接学生基本信息，并过滤掉循环长度小于3的结果
+    final_output AS (
+        SELECT
+            s.student_id,
+            s.student_name,
+            s.major,
+            pd.cycle_length,
+            pd.total_hours AS total_study_hours
+        FROM
+            pattern_detected pd
+            JOIN students s ON s.student_id = pd.student_id
+        WHERE pd.cycle_length >= 3
+    )
+-- 第七步：输出结果，并按循环长度和总学习时长降序排列
+SELECT *
+FROM final_output
+ORDER BY cycle_length DESC, total_study_hours DESC;
+```
+
+#### Pandas
+
+```python
+import pandas as pd
+from datetime import timedelta
+
+
+def find_study_spiral_pattern(
+    students: pd.DataFrame, study_sessions: pd.DataFrame
+) -> pd.DataFrame:
+    study_sessions["session_date"] = pd.to_datetime(study_sessions["session_date"])
+
+    result = []
+
+    for student_id, group in study_sessions.groupby("student_id"):
+        # 按日期排序
+        group = group.sort_values("session_date").reset_index(drop=True)
+
+        # 用于记录当前连续段
+        temp = []
+        last_date = None
+
+        for idx, row in group.iterrows():
+            if not temp:
+                temp.append(row)
+            else:
+                delta = (row["session_date"] - last_date).days
+                if delta <= 2:
+                    temp.append(row)
+                else:
+                    # 处理之前的连续段
+                    if len(temp) >= 6:
+                        _check_pattern(student_id, temp, result)
+                    temp = [row]
+            last_date = row["session_date"]
+
+        # 最后一个连续段
+        if len(temp) >= 6:
+            _check_pattern(student_id, temp, result)
+
+    # 构造结果 DataFrame
+    df_result = pd.DataFrame(
+        result, columns=["student_id", "cycle_length", "total_study_hours"]
+    )
+
+    if df_result.empty:
+        return pd.DataFrame(
+            columns=[
+                "student_id",
+                "student_name",
+                "major",
+                "cycle_length",
+                "total_study_hours",
+            ]
+        )
+
+    # 合并 student_name 和 major
+    df_result = df_result.merge(students, on="student_id")
+
+    df_result = df_result[
+        ["student_id", "student_name", "major", "cycle_length", "total_study_hours"]
+    ]
+
+    return df_result.sort_values(
+        by=["cycle_length", "total_study_hours"], ascending=[False, False]
+    ).reset_index(drop=True)
+
+
+def _check_pattern(student_id, sessions, result):
+    subjects = [row["subject"] for row in sessions]
+    hours = sum(row["hours_studied"] for row in sessions)
+
+    n = len(subjects)
+    for cycle_len in range(3, n // 2 + 1):
+        if n % cycle_len != 0:
+            continue
+        first_cycle = subjects[:cycle_len]
+        is_pattern = True
+        for i in range(1, n // cycle_len):
+            if subjects[i * cycle_len : (i + 1) * cycle_len] != first_cycle:
+                is_pattern = False
+                break
+        if is_pattern:
+            result.append(
+                {
+                    "student_id": student_id,
+                    "cycle_length": cycle_len,
+                    "total_study_hours": hours,
+                }
+            )
+            break  # 只记录最长周期的第一个匹配
+```
+
+<!-- tabs:end -->
+
+<!-- solution:end -->
+
+<!-- problem:end -->
diff --git a/solution/3600-3699/3617.Find Students with Study Spiral Pattern/README_EN.md b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/README_EN.md
new file mode 100644
index 0000000000000..00bc4140329ff
--- /dev/null
+++ b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/README_EN.md	
@@ -0,0 +1,430 @@
+---
+comments: true
+difficulty: Hard
+edit_url: https://github.com/doocs/leetcode/edit/main/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README_EN.md
+tags:
+    - Database
+---
+
+<!-- problem:start -->
+
+# [3617. Find Students with Study Spiral Pattern](https://leetcode.com/problems/find-students-with-study-spiral-pattern)
+
+[中文文档](/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README.md)
+
+## Description
+
+<!-- description:start -->
+
+<p>Table: <code>students</code></p>
+
+<pre>
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| student_id   | int     |
+| student_name | varchar |
+| major        | varchar |
++--------------+---------+
+student_id is the unique identifier for this table.
+Each row contains information about a student and their academic major.
+</pre>
+
+<p>Table: <code>study_sessions</code></p>
+
+<pre>
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| session_id    | int     |
+| student_id    | int     |
+| subject       | varchar |
+| session_date  | date    |
+| hours_studied | decimal |
++---------------+---------+
+session_id is the unique identifier for this table.
+Each row represents a study session by a student for a specific subject.
+</pre>
+
+<p>Write a solution to find students who follow the <strong>Study Spiral Pattern</strong>&nbsp;- students who consistently study multiple subjects in a rotating cycle.</p>
+
+<ul>
+	<li>A Study Spiral Pattern means a student studies at least <code>3</code><strong> different subjects</strong> in a repeating sequence</li>
+	<li>The pattern must repeat for <strong>at least </strong><code>2</code><strong> complete cycles</strong> (minimum <code>6</code> study sessions)</li>
+	<li>Sessions must be <strong>consecutive dates</strong> with no gaps longer than <code>2</code> days between sessions</li>
+	<li>Calculate the <strong>cycle length</strong> (number of different subjects in the pattern)</li>
+	<li>Calculate the <strong>total study hours</strong> across all sessions in the pattern</li>
+	<li>Only include students with cycle length of <strong>at least </strong><code>3</code><strong> subjects</strong></li>
+</ul>
+
+<p>Return <em>the result table ordered by cycle length in <strong>descending</strong> order, then by total study hours in <strong>descending</strong> order</em>.</p>
+
+<p>The result format is in the following example.</p>
+
+<p>&nbsp;</p>
+<p><strong class="example">Example:</strong></p>
+
+<div class="example-block">
+<p><strong>Input:</strong></p>
+
+<p>students table:</p>
+
+<pre class="example-io">
++------------+--------------+------------------+
+| student_id | student_name | major            |
++------------+--------------+------------------+
+| 1          | Alice Chen   | Computer Science |
+| 2          | Bob Johnson  | Mathematics      |
+| 3          | Carol Davis  | Physics          |
+| 4          | David Wilson | Chemistry        |
+| 5          | Emma Brown   | Biology          |
++------------+--------------+------------------+
+</pre>
+
+<p>study_sessions table:</p>
+
+<pre class="example-io">
++------------+------------+------------+--------------+---------------+
+| session_id | student_id | subject    | session_date | hours_studied |
++------------+------------+------------+--------------+---------------+
+| 1          | 1          | Math       | 2023-10-01   | 2.5           |
+| 2          | 1          | Physics    | 2023-10-02   | 3.0           |
+| 3          | 1          | Chemistry  | 2023-10-03   | 2.0           |
+| 4          | 1          | Math       | 2023-10-04   | 2.5           |
+| 5          | 1          | Physics    | 2023-10-05   | 3.0           |
+| 6          | 1          | Chemistry  | 2023-10-06   | 2.0           |
+| 7          | 2          | Algebra    | 2023-10-01   | 4.0           |
+| 8          | 2          | Calculus   | 2023-10-02   | 3.5           |
+| 9          | 2          | Statistics | 2023-10-03   | 2.5           |
+| 10         | 2          | Geometry   | 2023-10-04   | 3.0           |
+| 11         | 2          | Algebra    | 2023-10-05   | 4.0           |
+| 12         | 2          | Calculus   | 2023-10-06   | 3.5           |
+| 13         | 2          | Statistics | 2023-10-07   | 2.5           |
+| 14         | 2          | Geometry   | 2023-10-08   | 3.0           |
+| 15         | 3          | Biology    | 2023-10-01   | 2.0           |
+| 16         | 3          | Chemistry  | 2023-10-02   | 2.5           |
+| 17         | 3          | Biology    | 2023-10-03   | 2.0           |
+| 18         | 3          | Chemistry  | 2023-10-04   | 2.5           |
+| 19         | 4          | Organic    | 2023-10-01   | 3.0           |
+| 20         | 4          | Physical   | 2023-10-05   | 2.5           |
++------------+------------+------------+--------------+---------------+
+</pre>
+
+<p><strong>Output:</strong></p>
+
+<pre class="example-io">
++------------+--------------+------------------+--------------+-------------------+
+| student_id | student_name | major            | cycle_length | total_study_hours |
++------------+--------------+------------------+--------------+-------------------+
+| 2          | Bob Johnson  | Mathematics      | 4            | 26.0              |
+| 1          | Alice Chen   | Computer Science | 3            | 15.0              |
++------------+--------------+------------------+--------------+-------------------+
+</pre>
+
+<p><strong>Explanation:</strong></p>
+
+<ul>
+	<li><strong>Alice Chen (student_id = 1):</strong>
+
+    <ul>
+    	<li>Study sequence: Math &rarr; Physics &rarr; Chemistry &rarr; Math &rarr; Physics &rarr; Chemistry</li>
+    	<li>Pattern: 3 subjects (Math, Physics, Chemistry) repeating for 2 complete cycles</li>
+    	<li>Consecutive dates: Oct 1-6 with no gaps &gt; 2 days</li>
+    	<li>Cycle length: 3 subjects</li>
+    	<li>Total hours: 2.5 + 3.0 + 2.0 + 2.5 + 3.0 + 2.0 = 15.0 hours</li>
+    </ul>
+    </li>
+    <li><strong>Bob Johnson (student_id = 2):</strong>
+    <ul>
+    	<li>Study sequence: Algebra &rarr; Calculus &rarr; Statistics &rarr; Geometry &rarr; Algebra &rarr; Calculus &rarr; Statistics &rarr; Geometry</li>
+    	<li>Pattern: 4 subjects (Algebra, Calculus, Statistics, Geometry) repeating for 2 complete cycles</li>
+    	<li>Consecutive dates: Oct 1-8 with no gaps &gt; 2 days</li>
+    	<li>Cycle length: 4 subjects</li>
+    	<li>Total hours: 4.0 + 3.5 + 2.5 + 3.0 + 4.0 + 3.5 + 2.5 + 3.0 = 26.0&nbsp;hours</li>
+    </ul>
+    </li>
+    <li><strong>Students not included:</strong>
+    <ul>
+    	<li>Carol Davis (student_id = 3): Only 2 subjects (Biology, Chemistry) - doesn&#39;t meet minimum 3 subjects requirement</li>
+    	<li>David Wilson (student_id = 4): Only 2 study sessions with a 4-day gap - doesn&#39;t meet consecutive dates requirement</li>
+    	<li>Emma Brown (student_id = 5): No study sessions recorded</li>
+    </ul>
+    </li>
+
+</ul>
+
+<p>The result table is ordered by cycle_length in descending order, then by total_study_hours in descending order.</p>
+</div>
+
+<!-- description:end -->
+
+## Solutions
+
+<!-- solution:start -->
+
+### Solution 1
+
+<!-- tabs:start -->
+
+#### MySQL
+
+```sql
+# Write your MySQL query statement below
+WITH
+    ranked_sessions AS (
+        SELECT
+            s.student_id,
+            ss.session_date,
+            ss.subject,
+            ss.hours_studied,
+            ROW_NUMBER() OVER (
+                PARTITION BY s.student_id
+                ORDER BY ss.session_date
+            ) AS rn
+        FROM
+            study_sessions ss
+            JOIN students s ON s.student_id = ss.student_id
+    ),
+    grouped_sessions AS (
+        SELECT
+            *,
+            DATEDIFF(
+                session_date,
+                LAG(session_date) OVER (
+                    PARTITION BY student_id
+                    ORDER BY session_date
+                )
+            ) AS date_diff
+        FROM ranked_sessions
+    ),
+    session_groups AS (
+        SELECT
+            *,
+            SUM(
+                CASE
+                    WHEN date_diff > 2
+                    OR date_diff IS NULL THEN 1
+                    ELSE 0
+                END
+            ) OVER (
+                PARTITION BY student_id
+                ORDER BY session_date
+            ) AS group_id
+        FROM grouped_sessions
+    ),
+    valid_sequences AS (
+        SELECT
+            student_id,
+            group_id,
+            COUNT(*) AS session_count,
+            GROUP_CONCAT(subject ORDER BY session_date) AS subject_sequence,
+            SUM(hours_studied) AS total_hours
+        FROM session_groups
+        GROUP BY student_id, group_id
+        HAVING session_count >= 6
+    ),
+    pattern_detected AS (
+        SELECT
+            vs.student_id,
+            vs.total_hours,
+            vs.subject_sequence,
+            COUNT(
+                DISTINCT
+                SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', n), ',', -1)
+            ) AS cycle_length
+        FROM
+            valid_sequences vs
+            JOIN (
+                SELECT a.N + b.N * 10 + 1 AS n
+                FROM
+                    (
+                        SELECT 0 AS N
+                        UNION
+                        SELECT 1
+                        UNION
+                        SELECT 2
+                        UNION
+                        SELECT 3
+                        UNION
+                        SELECT 4
+                        UNION
+                        SELECT 5
+                        UNION
+                        SELECT 6
+                        UNION
+                        SELECT 7
+                        UNION
+                        SELECT 8
+                        UNION
+                        SELECT 9
+                    ) a,
+                    (
+                        SELECT 0 AS N
+                        UNION
+                        SELECT 1
+                        UNION
+                        SELECT 2
+                        UNION
+                        SELECT 3
+                        UNION
+                        SELECT 4
+                        UNION
+                        SELECT 5
+                        UNION
+                        SELECT 6
+                        UNION
+                        SELECT 7
+                        UNION
+                        SELECT 8
+                        UNION
+                        SELECT 9
+                    ) b
+            ) nums
+                ON n <= 10
+        WHERE
+            -- Check if the sequence repeats every `k` items, for some `k >= 3` and divides session_count exactly
+            -- We simplify by checking the start and middle halves are equal
+            LENGTH(subject_sequence) > 0
+            AND LOCATE(',', subject_sequence) > 0
+            AND (
+                -- For cycle length 3:
+                subject_sequence LIKE CONCAT(
+                    SUBSTRING_INDEX(subject_sequence, ',', 3),
+                    ',',
+                    SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', 6), ',', -3),
+                    '%'
+                )
+                OR subject_sequence LIKE CONCAT(
+                    -- For cycle length 4:
+                    SUBSTRING_INDEX(subject_sequence, ',', 4),
+                    ',',
+                    SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', 8), ',', -4),
+                    '%'
+                )
+            )
+        GROUP BY vs.student_id, vs.total_hours, vs.subject_sequence
+    ),
+    final_output AS (
+        SELECT
+            s.student_id,
+            s.student_name,
+            s.major,
+            pd.cycle_length,
+            pd.total_hours AS total_study_hours
+        FROM
+            pattern_detected pd
+            JOIN students s ON s.student_id = pd.student_id
+        WHERE pd.cycle_length >= 3
+    )
+SELECT *
+FROM final_output
+ORDER BY cycle_length DESC, total_study_hours DESC;
+```
+
+#### Pandas
+
+```python
+import pandas as pd
+from datetime import timedelta
+
+
+def find_study_spiral_pattern(
+    students: pd.DataFrame, study_sessions: pd.DataFrame
+) -> pd.DataFrame:
+    # Convert session_date to datetime
+    study_sessions["session_date"] = pd.to_datetime(study_sessions["session_date"])
+
+    result = []
+
+    # Group study sessions by student
+    for student_id, group in study_sessions.groupby("student_id"):
+        # Sort sessions by date
+        group = group.sort_values("session_date").reset_index(drop=True)
+
+        temp = []  # Holds current contiguous segment
+        last_date = None
+
+        for idx, row in group.iterrows():
+            if not temp:
+                temp.append(row)
+            else:
+                delta = (row["session_date"] - last_date).days
+                if delta <= 2:
+                    temp.append(row)
+                else:
+                    # Check the previous contiguous segment
+                    if len(temp) >= 6:
+                        _check_pattern(student_id, temp, result)
+                    temp = [row]
+            last_date = row["session_date"]
+
+        # Check the final segment
+        if len(temp) >= 6:
+            _check_pattern(student_id, temp, result)
+
+    # Build result DataFrame
+    df_result = pd.DataFrame(
+        result, columns=["student_id", "cycle_length", "total_study_hours"]
+    )
+
+    if df_result.empty:
+        return pd.DataFrame(
+            columns=[
+                "student_id",
+                "student_name",
+                "major",
+                "cycle_length",
+                "total_study_hours",
+            ]
+        )
+
+    # Join with students table to get name and major
+    df_result = df_result.merge(students, on="student_id")
+
+    df_result = df_result[
+        ["student_id", "student_name", "major", "cycle_length", "total_study_hours"]
+    ]
+
+    return df_result.sort_values(
+        by=["cycle_length", "total_study_hours"], ascending=[False, False]
+    ).reset_index(drop=True)
+
+
+def _check_pattern(student_id, sessions, result):
+    subjects = [row["subject"] for row in sessions]
+    hours = sum(row["hours_studied"] for row in sessions)
+
+    n = len(subjects)
+
+    # Try possible cycle lengths from 3 up to half of the sequence
+    for cycle_len in range(3, n // 2 + 1):
+        if n % cycle_len != 0:
+            continue
+
+        # Extract the first cycle
+        first_cycle = subjects[:cycle_len]
+        is_pattern = True
+
+        # Compare each following cycle with the first
+        for i in range(1, n // cycle_len):
+            if subjects[i * cycle_len : (i + 1) * cycle_len] != first_cycle:
+                is_pattern = False
+                break
+
+        # If a repeated cycle is detected, store the result
+        if is_pattern:
+            result.append(
+                {
+                    "student_id": student_id,
+                    "cycle_length": cycle_len,
+                    "total_study_hours": hours,
+                }
+            )
+            break  # Stop at the first valid cycle found
+```
+
+<!-- tabs:end -->
+
+<!-- solution:end -->
+
+<!-- problem:end -->
diff --git a/solution/3600-3699/3617.Find Students with Study Spiral Pattern/Solution.py b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/Solution.py
new file mode 100644
index 0000000000000..d01704a9c4eb3
--- /dev/null
+++ b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/Solution.py	
@@ -0,0 +1,97 @@
+import pandas as pd
+from datetime import timedelta
+
+
+def find_study_spiral_pattern(
+    students: pd.DataFrame, study_sessions: pd.DataFrame
+) -> pd.DataFrame:
+    # Convert session_date to datetime
+    study_sessions["session_date"] = pd.to_datetime(study_sessions["session_date"])
+
+    result = []
+
+    # Group study sessions by student
+    for student_id, group in study_sessions.groupby("student_id"):
+        # Sort sessions by date
+        group = group.sort_values("session_date").reset_index(drop=True)
+
+        temp = []  # Holds current contiguous segment
+        last_date = None
+
+        for idx, row in group.iterrows():
+            if not temp:
+                temp.append(row)
+            else:
+                delta = (row["session_date"] - last_date).days
+                if delta <= 2:
+                    temp.append(row)
+                else:
+                    # Check the previous contiguous segment
+                    if len(temp) >= 6:
+                        _check_pattern(student_id, temp, result)
+                    temp = [row]
+            last_date = row["session_date"]
+
+        # Check the final segment
+        if len(temp) >= 6:
+            _check_pattern(student_id, temp, result)
+
+    # Build result DataFrame
+    df_result = pd.DataFrame(
+        result, columns=["student_id", "cycle_length", "total_study_hours"]
+    )
+
+    if df_result.empty:
+        return pd.DataFrame(
+            columns=[
+                "student_id",
+                "student_name",
+                "major",
+                "cycle_length",
+                "total_study_hours",
+            ]
+        )
+
+    # Join with students table to get name and major
+    df_result = df_result.merge(students, on="student_id")
+
+    df_result = df_result[
+        ["student_id", "student_name", "major", "cycle_length", "total_study_hours"]
+    ]
+
+    return df_result.sort_values(
+        by=["cycle_length", "total_study_hours"], ascending=[False, False]
+    ).reset_index(drop=True)
+
+
+def _check_pattern(student_id, sessions, result):
+    subjects = [row["subject"] for row in sessions]
+    hours = sum(row["hours_studied"] for row in sessions)
+
+    n = len(subjects)
+
+    # Try possible cycle lengths from 3 up to half of the sequence
+    for cycle_len in range(3, n // 2 + 1):
+        if n % cycle_len != 0:
+            continue
+
+        # Extract the first cycle
+        first_cycle = subjects[:cycle_len]
+        is_pattern = True
+
+        # Compare each following cycle with the first
+        for i in range(1, n // cycle_len):
+            if subjects[i * cycle_len : (i + 1) * cycle_len] != first_cycle:
+                is_pattern = False
+                break
+
+        # If a repeated cycle is detected, store the result
+        if is_pattern:
+            result.append(
+                {
+                    "student_id": student_id,
+                    "cycle_length": cycle_len,
+                    "total_study_hours": hours,
+                }
+            )
+            break  # Stop at the first valid cycle found
diff --git a/solution/3600-3699/3617.Find Students with Study Spiral Pattern/Solution.sql b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/Solution.sql
new file mode 100644
index 0000000000000..2e14c17e3d626
--- /dev/null
+++ b/solution/3600-3699/3617.Find Students with Study Spiral Pattern/Solution.sql	
@@ -0,0 +1,150 @@
+# Write your MySQL query statement below
+WITH
+    ranked_sessions AS (
+        SELECT
+            s.student_id,
+            ss.session_date,
+            ss.subject,
+            ss.hours_studied,
+            ROW_NUMBER() OVER (
+                PARTITION BY s.student_id
+                ORDER BY ss.session_date
+            ) AS rn
+        FROM
+            study_sessions ss
+            JOIN students s ON s.student_id = ss.student_id
+    ),
+    grouped_sessions AS (
+        SELECT
+            *,
+            DATEDIFF(
+                session_date,
+                LAG(session_date) OVER (
+                    PARTITION BY student_id
+                    ORDER BY session_date
+                )
+            ) AS date_diff
+        FROM ranked_sessions
+    ),
+    session_groups AS (
+        SELECT
+            *,
+            SUM(
+                CASE
+                    WHEN date_diff > 2
+                    OR date_diff IS NULL THEN 1
+                    ELSE 0
+                END
+            ) OVER (
+                PARTITION BY student_id
+                ORDER BY session_date
+            ) AS group_id
+        FROM grouped_sessions
+    ),
+    valid_sequences AS (
+        SELECT
+            student_id,
+            group_id,
+            COUNT(*) AS session_count,
+            GROUP_CONCAT(subject ORDER BY session_date) AS subject_sequence,
+            SUM(hours_studied) AS total_hours
+        FROM session_groups
+        GROUP BY student_id, group_id
+        HAVING session_count >= 6
+    ),
+    pattern_detected AS (
+        SELECT
+            vs.student_id,
+            vs.total_hours,
+            vs.subject_sequence,
+            COUNT(
+                DISTINCT
+                SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', n), ',', -1)
+            ) AS cycle_length
+        FROM
+            valid_sequences vs
+            JOIN (
+                SELECT a.N + b.N * 10 + 1 AS n
+                FROM
+                    (
+                        SELECT 0 AS N
+                        UNION
+                        SELECT 1
+                        UNION
+                        SELECT 2
+                        UNION
+                        SELECT 3
+                        UNION
+                        SELECT 4
+                        UNION
+                        SELECT 5
+                        UNION
+                        SELECT 6
+                        UNION
+                        SELECT 7
+                        UNION
+                        SELECT 8
+                        UNION
+                        SELECT 9
+                    ) a,
+                    (
+                        SELECT 0 AS N
+                        UNION
+                        SELECT 1
+                        UNION
+                        SELECT 2
+                        UNION
+                        SELECT 3
+                        UNION
+                        SELECT 4
+                        UNION
+                        SELECT 5
+                        UNION
+                        SELECT 6
+                        UNION
+                        SELECT 7
+                        UNION
+                        SELECT 8
+                        UNION
+                        SELECT 9
+                    ) b
+            ) nums
+                ON n <= 10
+        WHERE
+            -- Check if the sequence repeats every `k` items, for some `k >= 3` and divides session_count exactly
+            -- We simplify by checking the start and middle halves are equal
+            LENGTH(subject_sequence) > 0
+            AND LOCATE(',', subject_sequence) > 0
+            AND (
+                -- For cycle length 3:
+                subject_sequence LIKE CONCAT(
+                    SUBSTRING_INDEX(subject_sequence, ',', 3),
+                    ',',
+                    SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', 6), ',', -3),
+                    '%'
+                )
+                OR subject_sequence LIKE CONCAT(
+                    -- For cycle length 4:
+                    SUBSTRING_INDEX(subject_sequence, ',', 4),
+                    ',',
+                    SUBSTRING_INDEX(SUBSTRING_INDEX(subject_sequence, ',', 8), ',', -4),
+                    '%'
+                )
+            )
+        GROUP BY vs.student_id, vs.total_hours, vs.subject_sequence
+    ),
+    final_output AS (
+        SELECT
+            s.student_id,
+            s.student_name,
+            s.major,
+            pd.cycle_length,
+            pd.total_hours AS total_study_hours
+        FROM
+            pattern_detected pd
+            JOIN students s ON s.student_id = pd.student_id
+        WHERE pd.cycle_length >= 3
+    )
+SELECT *
+FROM final_output
+ORDER BY cycle_length DESC, total_study_hours DESC;
diff --git a/solution/DATABASE_README.md b/solution/DATABASE_README.md
index 6df4c2e40eb06..6e259ae79c78a 100644
--- a/solution/DATABASE_README.md
+++ b/solution/DATABASE_README.md
@@ -322,6 +322,7 @@
 | 3586 | [寻找 COVID 康复患者](/solution/3500-3599/3586.Find%20COVID%20Recovery%20Patients/README.md)                                                                 | `数据库` | 中等 |      |
 | 3601 | [寻找燃油效率提升的驾驶员](/solution/3600-3699/3601.Find%20Drivers%20with%20Improved%20Fuel%20Efficiency/README.md)                                          | `数据库` | 中等 |      |
 | 3611 | [查找超预订员工](/solution/3600-3699/3611.Find%20Overbooked%20Employees/README.md)                                                                           |          | 中等 |      |
+| 3617 | [Find Students with Study Spiral Pattern](/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README.md)                              |          | 困难 |      |
 
 ## 版权
 
diff --git a/solution/DATABASE_README_EN.md b/solution/DATABASE_README_EN.md
index d00b8d980391d..c7fe9f1d9290c 100644
--- a/solution/DATABASE_README_EN.md
+++ b/solution/DATABASE_README_EN.md
@@ -320,6 +320,7 @@ Press <kbd>Control</kbd> + <kbd>F</kbd>(or <kbd>Command</kbd> + <kbd>F</kbd> on
 | 3586 | [Find COVID Recovery Patients](/solution/3500-3599/3586.Find%20COVID%20Recovery%20Patients/README_EN.md)                                                                                     | `Database` | Medium     |        |
 | 3601 | [Find Drivers with Improved Fuel Efficiency](/solution/3600-3699/3601.Find%20Drivers%20with%20Improved%20Fuel%20Efficiency/README_EN.md)                                                     | `Database` | Medium     |        |
 | 3611 | [Find Overbooked Employees](/solution/3600-3699/3611.Find%20Overbooked%20Employees/README_EN.md)                                                                                             |            | Medium     |        |
+| 3617 | [Find Students with Study Spiral Pattern](/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README_EN.md)                                                           |            | Hard       |        |
 
 ## Copyright
 
diff --git a/solution/README.md b/solution/README.md
index b06c5cb059814..b4f539458e615 100644
--- a/solution/README.md
+++ b/solution/README.md
@@ -3627,6 +3627,7 @@
 |  3614  |  [用特殊操作处理字符串 II](/solution/3600-3699/3614.Process%20String%20with%20Special%20Operations%20II/README.md)  |    |  困难  |  第 458 场周赛  |
 |  3615  |  [图中的最长回文路径](/solution/3600-3699/3615.Longest%20Palindromic%20Path%20in%20Graph/README.md)  |    |  困难  |  第 458 场周赛  |
 |  3616  |  [Number of Student Replacements](/solution/3600-3699/3616.Number%20of%20Student%20Replacements/README.md)  |    |  中等  |  🔒  |
+|  3617  |  [Find Students with Study Spiral Pattern](/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README.md)  |    |  困难  |    |
 
 ## 版权
 
diff --git a/solution/README_EN.md b/solution/README_EN.md
index 565b58ac48d8d..d2d2c23c4c4e1 100644
--- a/solution/README_EN.md
+++ b/solution/README_EN.md
@@ -3625,6 +3625,7 @@ Press <kbd>Control</kbd> + <kbd>F</kbd>(or <kbd>Command</kbd> + <kbd>F</kbd> on
 |  3614  |  [Process String with Special Operations II](/solution/3600-3699/3614.Process%20String%20with%20Special%20Operations%20II/README_EN.md)  |    |  Hard  |  Weekly Contest 458  |
 |  3615  |  [Longest Palindromic Path in Graph](/solution/3600-3699/3615.Longest%20Palindromic%20Path%20in%20Graph/README_EN.md)  |    |  Hard  |  Weekly Contest 458  |
 |  3616  |  [Number of Student Replacements](/solution/3600-3699/3616.Number%20of%20Student%20Replacements/README_EN.md)  |    |  Medium  |  🔒  |
+|  3617  |  [Find Students with Study Spiral Pattern](/solution/3600-3699/3617.Find%20Students%20with%20Study%20Spiral%20Pattern/README_EN.md)  |    |  Hard  |    |
 
 ## Copyright