Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Area of Science:

• Sports Science
• Artificial Intelligence
• Educational Technology

Background:

• Traditional basketball coaching methods are insufficient for teaching complex tactical cooperation.
• There is a need for innovative systems to enhance students' understanding and application of basketball tactics.

Purpose of the Study:

• To design and evaluate an artificial neural network-based system for teaching basic basketball tactics cooperation.
• To improve students' tactical understanding and on-court performance through an integrated learning and training platform.

Main Methods:

• Utilized a convolutional neural network for classifying basketball game events from videos.
• Developed a BP neural network for an online basketball tactics training platform.
• Integrated teacher explanations and a tactical scoring module for comprehensive assessment.

Main Results:

• Convolutional neural network accuracy improved by 22.48% with motion pattern integration; average event classification accuracy reached 62.35%.
• BP neural network achieved 75% recognition rate with efficient processing.
• Students using the system showed significantly higher scores in tactical cooperation compared to the control group.

Conclusions:

• The artificial neural network-based system effectively enhances students' basic basketball tactical cooperation ability.
• The system offers a promising approach to improving sports education through AI integration.
• The developed modules provide a scalable solution for basketball tactical training.