Research on Olympic Medal Prediction and Influencing Factors Based on Multi-Dimensional Feature Fusion and Machine Learning

To address the limitations of existing Olympic medal prediction models—which over-rely on macroeconomic indicators and lack interpretability—this study proposes a hybrid machine learning framework integrating multi-dimensional feature engineering and model interpretability. This article construct a 19-feature system incorporating historical medals, athlete gender ratios, elite coach attributes (simulated via Monte Carlo), and host-nation effects. Three synergistic models are developed: (1) A multiple linear regression predicting 2028 medal distributions (RMSE=0.25); (2) A LASSO-Logistic regression identifying first-time medal-winning nations (e.g., UAE and Samoa, probability >0.5); (3) A Gradient Boosting Tree with SHAP interpretability quantifying elite coaches’ contribution (SHAP mean=0.217, *p*<0.001). Hyperparameter optimization via RandomizedSearchCV achieves high accuracy (gold medal MAE=0.09, total medals MAE=0.25). Key innovations include dynamic feature fusion and micro-level coach impact quantification, providing actionable insights for Olympic resource allocation.