Aiming at the problem that the graph learning-based methods in point cloud completion are easy to lead to the smoothing phenomenon of point features when the number of network layers is deepened， thereby causing local geometric detail loss， a point cloud completion method based on smooth-sharpen graph convolution was proposed， so as to improve the completion quality of irregular point clouds. Firstly， the SA （Set Abstraction） layer in PointNet++ was cross-stacked with the point Transformer module to extract multi-level features and global shape features of the incomplete point cloud. Secondly， the graph structure was constructed by generating the sub-point features through point-wise splitting， and the local similarity and coordinate difference information between point clouds were captured by using smooth graph convolution and sharpen graph convolution， respectively， so as to retain key geometric details and achieve coarse completion. Finally， global shape features and the results of coarse completion were fused in the refinement module to generate high-resolution point clouds gradually. Experimental results show that the proposed method outperforms comparison methods ODGNet （Orthogonal Dictionary Guided shape completion Network） and CRA-PCN （Cross-Resolution Aggregation Point Completion Network） on the ShapeNet and PCN （Point Completion Network） datasets. Specifically， the proposed method has the CD-T （Chamfer Distance-TopNet）， CD-P （Chamfer Distance-Point completion network） and F1-Score increased by at least 0.220， 0.285 and 0.009 on the PCN dataset， respectively， and the CD-P and F1-Score increased by at least 0.021 and 0.003 on the ShapeNet extended dataset， indicating that the proposed method has more advantages in geometric structure recovery. In addition， the completion results on the real-world KITTI （Karlsruhe Institute of Technology Toyota Technological Institute at Chicago） dataset further show that the proposed method can reconstruct the vehicle contour more completely， avoid blurring and artifacts effectively， and improve the effect of point cloud completion.