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By combining image processing and keypoint detection techniques, the model effectively recognizes and analyzes basketball player actions, offering a significant improvement over conventional methods. Firstly, the collected action videos were subjected to data preprocessing, including image size adjustment, grayscale processing, and normalization; then, the DeepLabCut model was utilized for keypoint detection to identify the key action parts of basketball players; finally, the detection results were analyzed and validated through the calculation of biomechanical parameters in sports. The research results showed that DeepLabCut performed excellently in keypoint detection accuracy and processing speed. Compared to traditional methods, its average error was only 4.63 pixels and the average processing time was only 45.7 milliseconds. 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