Abstract
With the continuous development of information technology, conventional physical education teaching methods are no longer applicable. In order to ensure the objectivity of college physical education evaluation, this paper designs a set of college physical education management information system based on artificial intelligence technology. The weighting algorithm in the student performance evaluation module and teacher performance evaluation module in the system adopts the intelligent algorithm based on FNN neural network. Experimental verification shows that the intelligent algorithm based on the FNN neural network can effectively predict the students’ score in the national college physical education examination, which can provide a more objective basis for teacher performance evaluation.
Highlights
Wang Yuqing said in the study that physical education in colleges and universities in China is divided into physical education major, physical education major, and nonphysical education major [1]
Wen Jiao said in a study on college physical education that the premise for students to obtain graduation qualification is that their physical performance passes the national unified physical education examination [4]
LiuYanRu further explained to the college sports test that, in addition to competition events, running, jumping, throwing, flexibility, and other tests all pass the score line strictly [6]
Summary
Wang Yuqing said in the study that physical education in colleges and universities in China is divided into physical education major, physical education major, and nonphysical education major [1]. Yang Dapeng in his study proposed that, in the actual management of college students, students’ physical fitness test and physical education examination can directly reflect students’ physical education curriculum level [11]. E data sorted by the above minmax module are all data in the [0,1] interval, while mental health data such as SDS and SAS and physical examination result data are dimensionless data. In order to reduce the complexity of the statistical process of this module, when it is required to issue SDS and SAS evaluation, directly input the 1-point system results; that is, the original results are formed in the [0,1] interval, and the physical examination results are directly issued in the 1point system results; that is, the original results are formed in the [0,1] interval. E statistical significance of FNN neural network is to summarize all the above input data into a double precision data as the actual evaluation result data of students.
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