This paper proposes an airborne light detection and ranging point cloud classification method that integrates the graph convolution model and PointNet to address the problem of low classification accuracy, which is caused by the lack of point local features description in the three-dimensional deep learning network PointNet. This method first determines the optimal neighborhood of each point using the minimum Shannon entropy criterion, and then the shallow features of each point are calculated to feed into the deep learning network. Second, the shallow features of point clouds are used to derive local features via a graph convolution operation, which are combined with the point-based and global features extracted by PointNet to obtain the feature vectors. Finally, the above features are combined to obtain the feature vectors. The proposed method is validated using the Vaihingen dataset provided by the International Society for Photogrammetry and Remote Sensing, and the experimental results show that the proposed method improves accuracy by 9.58 percentage points compared with the PointNet point cloud classification method.