Point cloud classification and segmentation are key steps in understanding three-dimensional (3D) scenes. Aiming at the problem that sparse point cloud input and occlusion cannot effectively identify point clouds, an improved classification and segmentation network linked-dynamic graph convolutional neural network (DGCNN) is proposed. First, the deep-level point cloud features were extracted by increasing the number of EdgeConv convolutional layers based on DGCNN. Next, the transformation networks of DGCNN were removed to simplify the network structure. Finally, the idea of introducing a deep residual network was used to link the output features of different network layers to form point cloud features, making the network training more stable. The proposed network was compared with other point cloud networks on ModelNet40 and ShapeNet Parts datasets. The experimental results show that the network has higher accuracy of point cloud classification and segmentation than other methods under the sparse point cloud input and occlusion. Besides, it has stronger robustness.