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In order to solve the problem that the existing 3D model recognition methods based on deep learning lack the contextual fine-grained local features of 3D models, which may cause the confusion of recognition with very similar geometric shapes and slightly different local details, a 3D model recognition method based on deep graph attention convolutional neural network is proposed. Firstly, the fine-grained local features of 3D models are mined by introducing a neighborhood selection mechanism. Secondly, the multi-scale spatial context information is captured by a spatial context coding mechanism, and is compensated with fine-grained local features to enhance the completeness of features. Finally, a multi-head mechanism is adopted to make the graph attention convolution layer aggregate features of multiple single-head to enhance the richness of features. In addition, a selective dropout algorithm is designed to prevent network overfitting, which ranks the importance of neurons according to value of the measure-ment weight, and intelligently discards those with lower importance. The accuracy of 3D model recognition on the ModelNet40 dataset of the algorithm in this paper reaches 92.6%, and the network complexity is low. The trade-off between accuracy rate of 3D model recognition and network complexity achieved by the proposed algorithm is superior to contemporary mainstream methods.