Design of Online Learning System of College Sports Gymnastics Based on BP Neural Network

Abstract

With the rapid development of artificial intelligence technology and big data analysis technology, the use of online learning has become more technical premises. It has the advantages of convenient data update, simple real-time communication, and high stability and flexibility. These have laid a good technical cornerstone for the development of online learning platforms. In order to solve the problems such as the lack of teaching materials for physical gymnastics in colleges and the lack of technical support for teaching, this study aims to study the design of college sports gymnastics online learning system based on BP neural network. On the basis of analyzing the introduction of BP neural network, the status quo of gymnastics teaching in Chinese colleges and universities and the principles of system design and adopting the principles of reliability, stability, and maintainability as the method of system design, the gymnastics online learning system based on BP neural network is carried out. Finally, the performance of the constructed system is tested. The test results show that, as the number of concurrent users continues to increase, the response time is gradually increasing. When the number of concurrent users is 3000, the maximum response time is 1.832 s, the minimum response time is 1.473 s, and the average response time is 1.512 s, which can meet the needs of users when the amount of concurrency is large.