A Big Data-Based Multidimensional Analysis and Optimization Framework for Physical Education

This study explores the application of big data technology in physical education, developing an efficient and intelligent data analysis framework. By leveraging multidimensional motion data collection and analysis, combined with key techniques such as 3D directional energy feature extraction and motion template matching, the research achieves precise identification and correction of sports movements. To enhance algorithm performance, the study introduces optimization strategies, including hierarchical energy feature extraction, adaptive template matching, and temporal consistency checks, effectively reducing computational complexity while improving real-time performance and accuracy. Experimental results demonstrate that the optimized model significantly outperforms the baseline, with a 3%-5% improvement in detection resolution, a 30% reduction in processing time, and a 40% decrease in error rates. This research not only provides theoretical support and technical assurance for the reform of physical education models but also lays a foundation for the development of intelligent physical education. Future advancements integrating artificial intelligence technologies can further drive personalized and intelligent teaching practices.