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Hand pose estimation is one of the important research directions of computer vision, which plays an important role in human-computer interaction, virtual reality, robot control and other application fields. At present, hand pose estimation has the problem of single feature representation method. This paper proposes a feature construction method of hand key point connection relationship and a key point feature aggregation enhancement method based on hand motion semantic relationship to improve the hand feature representation and information sharing ability. Aiming at the occlusion problem in hand target detection and image segmentation, a hand contour feature extraction method is designed to improve the preprocessing effect. Based on the proposed multi- feature representation and enhancement method, a depth learning neural network model based on full convolution structure is constructed to avoid the problem of spatial information loss caused by direct regression calculation of 3D pose information, thus effectively improving the accuracy of 3D hand pose estimation. Compared with the SOTA model on DO, ED, RHD datasets, it has achieved a competitive effect, and the average AUC result has reached 93.3%, indicating that the proposed method also has good universality. [ABSTRACT FROM AUTHOR]