INTELLIGENT RECOGNITION OF PHYSICAL EDUCATION CURRICULUM RESOURCES BASED ON DEEP NEURAL NETWORK AND THE GAME MODEL STUDY

Abstract

Nowadays, with more and more physical education curriculum resources, schools or teachers have more and more choices for physical education curriculum resources. However, because some teachers need a deep understanding of curriculum training programs and standards, the selected curriculum resources cannot promote their curriculum development. This paper puts forward the research on the intelligent recognition and game model of physical education curriculum resources based on neural networks. The specific research conclusions are as follows: The intelligent consciousness and movement model of physical education curriculum resources based entirely on the technical knowledge of the BP neural community and deep neural community are proposed. With the help of MATLAB7.1 neural network toolbox to implement the specific recommendation system, a three-layer BP network is established, and the NEWFF function is used to create the neural network. Useful resources in each direction generate a direction recognition vector according to the route guidance standard, calculate the course recommendation degree according to selection statistics and scoring, and input the course resource recognition vector and recommendation degree into the neural network. When the number of hidden layer nodes is 10, and the learning training algorithm selects the L-M optimization algorithm, the error between the actual output and the expected output of the network meets the requirements. It shows that the accuracy of the recommendation model meets the requirements; that is, the relationship between the recognition vector of physical education course resources and the recommendation degree of course resources reflected by the neural network basically reflects the functional relationship between them and the model can be used to make corresponding recommendations.