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This study addresses the performance teaching needs of traditional Chinese Guzheng and attempts to introduce computer vision and deep learning technologies into gesture recognition tasks. By constructing a dataset that includes various Guzheng playing actions, image sequences are collected during the performance process. Combined with convolutional neural networks for feature extraction, this approach achieves automatic recognition of multiple basic gestures. The model employs an optimized ResNet50 structure, maintaining high recognition accuracy under standardized image input and weighted classifiers. Experiments show that the system performs stably in recognizing typical actions and has a certain tolerance for complex action transitions and partial hand occlusions. When deployed in educational settings, the system can provide real-time feedback and visual presentations, assisting teachers in evaluating students' gesture standards and enhancing interactive teaching effects. From the perspective of engineering implementation and practicality in education, this research provides methodological support for the integration of traditional arts and artificial intelligence, laying the groundwork for future intelligent musical instrument training systems. Overall results indicate that this technical approach holds practical significance and application potential in improving Guzheng performance quality and reducing teaching costs.
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