Abstract
The traditional basketball teaching mode cannot meet the needs of students for the basic cooperation of basketball tactics. Therefore, a basic cooperation teaching system of basketball tactics based on artificial neural network is studied and designed. The system has a professional basketball game video tactical learning module. The events in the basketball game video are classified through a convolutional neural network and combined with the explanation of teachers to make the students have an intuitive understanding of the basic cooperation of basketball tactics and then design the basketball game module based on a BP neural network to provide students with an online basketball tactics training platform. Finally, the teacher scores the performance of the actual on-site training students in the basic cooperation of basketball tactics through the tactical scoring module on the system. The results show that after the introduction of global and collective motion patterns, the classification accuracy of the convolutional neural network is improved by 22.48%, which has significant optimization. The average accuracy of basketball game video event classification is 62.35%, and the accuracy of snatch event classification is improved to 95.28%. The recognition rate of the BP neural network combined with momentum gradient descent method is 75%, the number of weight adjustment is less, and the memory is small while ensuring fast running speed. Students who accept the basic basketball tactics cooperation teaching system based on the artificial neural network for basketball teaching have an overall score of 27.99 ± 2.11 points The overall score of exchange defense cooperation was 24.12 ± 2.03, which was higher than that of the control group. The above results show that the basketball tactical basic cooperation teaching system based on the artificial neural network has a good teaching effect in improving students' basketball tactical basic cooperation ability.
Highlights
With the advent of the era of national fitness, basketball has become a popular sports option for college students
Traditional basketball teaching is difficult to meet the needs of students to improve the basic cooperation ability of basketball tactics. erefore, a convolution neural network is used to automatically analyze the events of basketball sports video to improve players’ understanding of the basic cooperation of basketball tactics. en a basketball game module based on a BP neural network is designed
Basketball tactics teaching is a difficult point in basketball teaching
Summary
With the advent of the era of national fitness, basketball has become a popular sports option for college students. Erefore, a convolution neural network is used to automatically analyze the events of basketball sports video to improve players’ understanding of the basic cooperation of basketball tactics. This paper puts forward an ANN-based basketball tactics basic cooperative teaching system, which is mainly composed of basketball tactics basic cooperative video learning module, basketball game training module, and basketball tactics basic cooperative ability scoring module to provide more effective ways to improve the students’ ability of basic basketball tactics. E GCMP classification method based on the convolutional neural network is used to intelligently classify and analyze basketball game video events so as to provide material basis for basketball tactics teaching. Using the BP neural network to design basketball game tactical decision-making training system, compared with other neural network models, it improves the AI intelligence and identification of game modules and provides a training platform for basketball tactical teaching, which is innovative and pioneering
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