An Intelligent Physical Training System in Football Education

With the advancement of intelligent technology, data-driven evaluation methods have gained increasing attention in physical education, particularly in the application of intelligent physical training systems in football education. These systems enable precise assessment of athletes’ training status and provide scientific support for personalized training, thereby enhancing training efficiency and game performance. This study employs Random Forest and Neural Network models to construct an intelligent evaluation system for predicting students’ overall performance in football training. Key performance indicators such as passing frequency, sprint speed, and shooting accuracy are collected and analyzed to determine their impact on comprehensive scores. Experimental results demonstrate that the Random Forest model excels in stability and interpretability, while the Neural Network achieves higher prediction accuracy in complex pattern recognition. The combination of both models enhances generalization ability and applicability. Additionally, feature importance analysis identifies sprint speed and shooting accuracy as the most critical factors influencing training performance. This study proposes data-driven training optimization strategies to help students improve their football training performance. The findings confirm that intelligent physical training systems can effectively support football education, promoting the development of personalized and refined training programs and providing strong technological support for modern sports education.