CONSTRUCTION AND OPERABILITY ANALYSIS OF INTELLIGENT NETWORK PHYSICAL EDUCATION TEACHING SYSTEM

For the low quality and slow efficiency of college physical education (PE) intelligent network teaching, a classification and evaluation method of college PE made large-scale open on-line courses (MOOC) mode is proposed. This method constructs a fuzzy ID3 decision tree model. On this basis, it uses triangular membership function and Kohonen feature mapping algorithm to discretise and fuzzify, and finally completes the evaluation of students' PE MOOC mode. The fuzzy ID3 exhibits the highest degree of accuracy in classification when the authenticity threshold is set at approximately 0.8. The classification accuracy indicates fuzzy ID3 in the four databases can obtain high accuracy. The classification accuracy of the training set of l-o database is 75.8%, 62.8%, 76.5%, and 95.0%. Fuzzy ID3 outperforms the minimum classifications uncertainty and yields better classification results for the l-o database with 18, 12, 16, and 10 classification rules, respectively. At an authenticity threshold of around 0.8, the fuzzy ID3 algorithm demonstrates the highest degree of accuracy for classification. The suggested MOOC model can extract a larger number of classification rules, which leads to better accuracy. In the future, the PE teaching model can be implanted in other colleges and universities.