## 1 Introduction
Additionally, evidence from another study suggests that changes at the policy level--such as the implementation of revised national school guidelines--may inadvertently lead to declines in physical activity and cardiorespiratory fitness among school-age populations [9]. \begin{table}
\begin{tabular}{c c} \hline
**Test Subjects** & **Individual Indicators** & **Weight (\%)** \\ \hline Body Mass Index (BMI) & 15 \\ Vital Capacity & 15 \\
2020 Students of Yunnan Agricultural & 50 m Sprint & 20 \\ University/Southwest Forestry University & Sit-and-Reach & 10 \\ Standing Long Jump & 10 \\ Pull-ups (Male)/1 min Sit-ups (Female) & 10 \\
1000 m Run (Male)/800 m Run (Female) & 20 \\ \hline \end{tabular} Note: Body Mass Index (BMI) = weight (kg)/height\({}^{2}\) (m\({}^{2}\)).  
\end{table} 
Table 1: National student physical health standards—test items and weights. Similar patterns have been identified in Korea, where a large-scale cross-sectional analysis demonstrated that both insufficient physical activity and prolonged sedentary behavior independently contribute to increased body weight among adolescents, emphasizing the complex interplay of lifestyle behaviors on youth health [10]. These findings collectively underscore the need for comprehensive, behaviorally and structurally informed interventions to counteract the global trend of declining youth fitness and health. However, despite large-scale data collection efforts in China and abroad, the mining and analysis of student physical fitness data remain relatively underdeveloped. Existing studies predominantly rely on traditional statistical methods or simple clustering algorithms (e.g., K-means) [11], which often struggle to process high-dimensional data or provide actionable insights. Furthermore, post-clustering interpretations and individualized intervention strategies are often lacking or poorly integrated. To address these limitations, recent research has explored more sophisticated machine learning methods. For instance, [PERSON] et al. applied a back-propagation neural network to model university students' health profiles, achieving a classification accuracy of 90.22% [11]. [PERSON] and [PERSON] combined convolutional neural networks with evolutionary algorithms to assess physical fitness more effectively [12]. In the international context, [PERSON] et al. employed Support Vector Machines (SVMs), Radial Basis Function Neural Networks (RBFNNs), and Tree Boosting to predict fitness levels in Turkish students, with SVM outperforming others in terms of prediction accuracy [13]. These studies demonstrate the growing potential of intelligent algorithms in physical health prediction and assessment. Nevertheless, several challenges remain. Clustering techniques like K-means require the prior specification of the number of clusters and suffer from instability due to random initialization. Studies by [PERSON] et al. and [PERSON] et al. attempted to improve the traditional K-means algorithm, yet issues such as suboptimal feature selection and interpretability persist [14; 15; 16]. Additionally, while machine learning models improve predictive performance, their integration into a cohesive framework for physical fitness profiling and intervention design is still evolving. Against this backdrop, the present study proposes an integrated analytical framework that combines improved K-means clustering with multiple machine learning models (Random Forest, decision tree, Gradient Boosting Tree, and logistic regression). After conducting univariate and multivariate analyses to ensure data consistency, the framework applies a dimension-reduction-enhanced K-means variant to identify meaningful health subgroups among college students. Classification models are subsequently used to validate the clustering structure and predict students' health trends. Based on the model outputs, targeted and interpretable intervention strategies are formulated to support personalized physical education and health promotion at the university level.