A Movement Evaluation and Personalized Guidance System for Martial Arts Teaching Using EfficientNet and Collaborative Filtering

Traditional research has difficulty in precisely evaluating students’ movements in martial arts teaching, and cannot provide targeted teaching suggestions based on students’ characteristics in personalized guidance. This study constructs a movement evaluation and personalized guidance system for martial arts teaching by integrating the EfficientNet model with a recommendation algorithm based on collaborative filtering. First, EfficientNet is used to perform multi-category classification evaluation on martial arts movements, and the cross-entropy loss function is used to improve the classification accuracy. Then, combined with the students’ historical movement performance, a collaborative filtering algorithm based on the Pearson correlation coefficient is used to recommend personalized training plans for each student. Grid search is used to optimize hyperparameters such as user neighborhood size and similarity measurement to ensure the accuracy and diversity of recommendation results. Experimental data show that after systematic teaching evaluation and personalized guidance, the average score of students increases by about 15.84%, and the average self-assessed satisfaction (1–5) of students after the recommended content reaches 4.1. The system can accurately evaluate students’ movement performance and provide targeted training suggestions based on individual needs.