Research on the Application of BP Neural Network Algorithm in the Practical Teaching of Public Physical Education in Colleges and Universities

This article studies the application of BP neural network algorithm in practical teaching of public physical education in universities. Through the introduction and exploration of BP neural network algorithm, combined with the characteristics and needs of public sports practice teaching in colleges and universities, this paper puts forward the method of using BP neural network algorithm to monitor and evaluate the state of Student activism’ sports, and applies and verifies it in actual teaching. Starting from the research goal of cultivating sports professionals, this paper systematically discusses the important concepts and interrelationships of practical ability, practical ability, and hands-on ability of sports major college students, as well as their understanding and basic viewpoints on this study. The BP neural network algorithm is a multi-layer feedforward network with learning and adaptability. In the monitoring and evaluation of exercise status, BP neural networks can be used to monitor and evaluate students’ exercise indicators such as heart rate, electrocardiogram, and posture. Due to the high accuracy and strong robustness of the BP neural network algorithm, it can effectively reduce the error rate of evaluation while ensuring accuracy. The application results show that the BP neural network algorithm has good effects in the practical teaching of public sports in universities, effectively constructing an efficient and accurate system for monitoring and evaluating sports status. This method provides a more autonomous, fast, and accurate monitoring and evaluation plan for sports status, providing valuable experience and reference for the teaching reform and innovation of public sports practical courses. In summary, this study demonstrates that the application of BP neural network algorithm in practical teaching of public physical education in universities has good results and application prospects. Based on this, it may be necessary to further deepen the research on motion state monitoring and evaluation using BP neural network algorithm in the future, explore more scientific and effective teaching methods, and make contributions to improving the quality and effectiveness of public physical education teaching in universities.