Multi Dimensional Evaluation of Middle School Students’ Physical and Mental Quality and Intelligent Recommendation of Exercise Programs Based on Big Data Analysis

Abstract

Taking into account that the traditional physical and mental quality education in schools is difficult to meet the comprehensive analysis and evaluation of the physical and psychological quality of middle school students, improve the enthusiasm of students to exercise physical and mental quality, let teachers and parents understand the physical and mental quality of students in real time, and provide students with physical and mental quality exercises and physical examinations. Personalized guidance for the new needs of middle school students’ physical and mental quality education. We use big data analysis technology to focus on the evaluation method of middle school students’ physical and mental quality and personalized exercise program recommendation method, and proposes a collaborative filtering recommendation framework based on graph neural network GNNCF (graph neural network based collaborative filtering), using Embedding technology and graph convolutional neural network to mine the attributes and interactive relationship features in the data, and then through the fusion of feature vector expressions to achieve personalized exercise program recommendations. The design and implementation of a mobile terminal-based monitoring and evaluation system for the physical and mental qualities of middle school students based on mobile terminals, “Qing Yue Circle”, which can provide services for students, teachers and parents respectively, has verified that it can meet the above requirements through a fixed-point test of Qing Yue Circle in colleges and universities New demand for physical and mental quality education for middle school students.