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

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dc.contributor.authorYang, Xuelin
dc.contributor.authorSumettikoon, Piyapong
dc.contributor.authorWu, Xiang
dc.date.accessioned2026-08-06T10:38:52Z
dc.date.available2026-08-06T10:38:52Z
dc.date.issued2023-01-01
dc.description.abstractNowadays, withmore 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 researchon the intelligent recognition and gamemodelof 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 deepneuralcommunityare proposed. WiththehelpofMATLAB7.1 neuralnetwork toolbox to implement the specificrecommendation system, a three-layerBPnetwork is established, and the NEWFF function is used to create the neural network. Useful resources in each direction generate a directionrecognition vector according to the route guidancestandard, calculate the course recommendation degree according to selection statistics and scoring, and input the courseresourcerecognitionvector and recommendationdegreeinto theneuralnetwork. 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.
dc.identifier.citationOperational Research in Engineering Sciences Theory and Applications, 6(3), 138-151, 2023
dc.identifier.doi10.31181/oresta/060307
dc.identifier.issn26201607
dc.identifier.other2-s2.0-85180602048
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13716
dc.sourceOperational Research in Engineering Sciences Theory and Applications
dc.subjectCourse identification vector
dc.subjectCourse recommendation
dc.subjectDeep learning
dc.subjectNeural network
dc.subjectPhysical education course
dc.titleINTELLIGENT RECOGNITION OF PHYSICAL EDUCATION CURRICULUM RESOURCES BASED ON DEEP NEURAL NETWORK AND THE GAME MODEL STUDY
dc.typeArticle

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