Knowledge graph construction for intelligent physical education and its application in training

Knowledge mapping, as an emerging knowledge management tool, provides a new perspective of knowledge learning for physical education teaching. In this study, knowledge mapping is introduced into physical education teaching, and a comprehensive physical education knowledge map is constructed by integrating the teaching resources and contents of physical education teaching and utilizing related techniques such as knowledge extraction and knowledge fusion. The method of fusion of sports knowledge graph is also proposed, including three parts: graph approximation, similarity calculation, and subgraph fusion. Finally, the constructed knowledge graph is practically applied, and a recommendation model based on sports knowledge graph and neural network is constructed to realize the sports teaching application of intelligent educational knowledge graph. The entity recognition module optimized the recognition accuracy rate on the objective existence entities of sports by 1.45%, and the relationship extraction module outperformed AGGCN in all three indicators. The training method of this paper is better than the MICT sports training method in improving students’ cardiorespiratory capacity and flexibility quality. The improvement of students’ 800m running performance under this paper’s training program is 0.13min more than that of MICT.It is proved that the sports course recommendation model based on knowledge graph and neural network provides a reference for the management and application of knowledge data in physical education, with a view to promoting the progress in the field of intelligent education.