An interpretable adaptive edge-weighted graph convolutional networks model for physical fitness assessment of primary school student in high-altitude regions

Physical fitness testing is a vital means of assessing the physical health of adolescents. However, existing assessment methods face limitations in processing complex multivariate fitness data, demonstrating poor generalizability and a lack of interpretability. To address these challenges, we propose an innovative Adaptive Edge Weight Graph Convolutional Neural Network (AWE-GCN) model. By combining dynamic graph convolutional neural networks with the SHAP (SHapley Additive exPlanations) interpretability method, this study achieves the first high-precision assessment of comprehensive physical fitness and analysis of key influencing indicators for primary school students in high-altitude regions. The model achieved macro-average F1 scores of 97.41% (male) and 96.37% (female) for 25,790 student records from Xining, Qinghai, China (average altitude: 3,137 m). The SHAP analysis identified the 50-meter sprint, 1-minute sit-ups, and the 50-meter × 8 shuttles run as core indicators influencing primary school students' physical fitness. It further elucidated the corresponding physiological mechanisms from the perspective of sensitive periods in physical development and high-altitude hypoxia adaptation. Furthermore, comparative experiments against four baseline models, including SVM, CatBoost, MLP and CNN, clearly demonstrate that the AWE-GCN model achieves the best performance across all key evaluation metrics, such as Precision, Recall, and F1-Score. The ablation study confirms that SMOTE provides a balanced data foundation, upon which the adaptive edge weighting module maximizes physical fitness classification performance, representing the model's key capability to dynamically capture the complex relationships among physiological indicators. By integrating deep learning with interpretability analysis, this study provides a high-precision framework for adolescent physical fitness assessment, with scientific significance for optimizing physical education curriculum design and formulating health policy.