In order to further improve the classification accuracy of ship classification method for point cloud images, a new ship classification method based on three-dimensional convolutional neural network (3D CNN) is proposed. First, the point cloud image is transformed into a voxel grid image by the density grid method and the voxel grid image is taken as the input object of a 3D CNN. Then, the high-level features of the voxel grid image are extracted by the designed 6-layer 3D CNN to capture its structural information. Finally, the classification results are obtained using the Softmax function in the output layer. The experimental results show that the classification accuracy of the proposed method can reach 96.14% on the self-build point cloud image ship dataset, 5.97% higher than that of the 3D ShapeNets method and 2.46% higher than that of the VoxNet method. Compared with some existing methods, the proposed method has higher classification accuracy on Sydney urban object dataset. These results show that the proposed method has a good classification performance.