PointNet model only extracts features of isolated points and therefore does not consider neighborhood structure information among points. To address this limitation, we propose GraphPNet, a 3D point cloud classification and segmentation model based on graph convolutional networks. The 3D point cloud is transformed into an undirected graph structure. Then, the neighborhood structure information of the 3D point cloud is obtained from the undirected graph structure. Classification and segmentation accuracy are improved by fusing neighborhood information with single point information. In classification experiments, GraphPNet is trained and tested on the ModelNet40 dataset and compared with VoxNet, PointNet, and 3D ShapeNets models. The results demonstrate that GraphPNet obtains better accuracy than the other models. In segmentation experiments, the ShapeNet dataset is used for training and testing, and the mean intersection over union values of GraphPNet and other segmentation models, such as PointNet, are compared. The results confirm the effectiveness of the proposed GraphPNet model.