Optimization Analysis of Physical Education Integration Teaching Based on Multi Support Vector Machines

The integration of sports and education serves as a core strategy for the coordinated development of youth well-being and academic growth in China’s new era. Its core goal is to achieve a deep integration of sports education and cultural education, and cultivate composite talents with comprehensive qualities and sports skills. In the current practice of integrating sports and education, there are problems such as imbalanced teaching objectives, lack of personalized curriculum design, incomplete teaching effect evaluation system, and insufficient adaptability of integration strategies. Traditional qualitative analysis and single quantitative methods are difficult to accurately identify the core influencing factors in the teaching process, and cannot provide scientific and quantifiable decision-making basis for teaching optimization. Support Vector Machine (SVM), as an efficient machine learning algorithm, has significant advantages in small sample, high-dimensional, and nonlinear data processing. Multi support vector machine fusion models can better improve the accuracy and stability of prediction and classification. By incorporating wearable sensing and teachingprocess data into a multi-support vector machine algorithm, this research establishes a comprehensive “data collection —feature extraction—model training—effect prediction—strategy optimization” system to refine the physical education integration process.