Affiliations: School of Physical Education, Anqing Normal University, Anqing, Anhui, China
Correspondence:
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Corresponding author. Wang Yunxing, School of Physical Education, Anqing Normal University, Anqing, Anhui, 246133, China. E-mail: [email protected].
Abstract: In a certain network environment, the use of teaching evaluation assistant decision-making system can further promote the rationality and fairness of teaching evaluation. Two screening algorithms are proposed, which combine with the influence factors in the automatic evaluation model of physical education teaching, delete the relevant factors and leave them behind. After two deep screening, the accuracy of the results is improved. By introducing the artificial neural network technology into the evaluation of physical education teachers’ teaching quality, the evaluation factors of neurons are calculated to establish the evaluation model of BP neural network. Secondly, the factors affecting the evaluation results in the evaluation model of BP neural network are decomposed and screened by using the second screening method, and a certain amount of training and learning is carried out for the teaching quality data. The experimental results show that the second screening algorithm is effective and can improve the accuracy of the results of automatic evaluation of physical education teaching. By establishing the automatic evaluation model of physical education teaching, it can provide reference for the evaluation and assistant decision-making of physical education teaching quality in Vocational colleges.
