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Aiming at the issue of complex 3D shape analysis and recognition, this paper presented a novel 3D graph convolu- tion classification method. It established a joint graph convolution learning mechanism of local geometry and global structure to provide both geometrical features and global context features, which effectively improved the robustness and stability of 3D data learning. Firstly, it constructed the local graph in spatial domain by farthest point sampling and K-nearest neighbor method, and designed a dynamic spectral graph convolution operator to extract local geometric features effectively. Meanwhile, it con- structed the global feature graph based on random sampling in the feature domain, and obtained the global structure context by spectral graph convolution. Furthermore, it established a weighted graph convolution network with an attention mechanism to achieve adaptive feature fusion. Finally, under the optimization of objective function, it improved the performance of feature learning effectively. Experimental results show that the proposed joint network learning mechanism, which combined local geo- metric features with global structure features, enhances the representation ability and discrimination of deep features, and ob- tains better recognition and classification performance compared with advanced methods. This method can be used for large- scale point clouds recognition, 3D shape reconstruction and data compression. It has important research significance and broad application prospects in robot, product digital analysis, intelligent navigation, virtual reality and other fields. [ABSTRACT FROM AUTHOR]