Point cloud is an important three-dimensional expression, and it has a wide range of applications in computer vision and robotics. Due to occlusion and uneven sampling in real application scenarios, the shape of the target object point cloud collected by the sensor is often incomplete. To achieve the point cloud of feature extraction and shape completion, a new point cloud completion network based on the multibranch structure is proposed in this paper. The encoder is primarily responsible for extracting the global and local features from the input information, and the multibranch structure in the decoder is responsible for converting the features to point clouds to obtain the complete point cloud shape of the object. Experiments are conducted using the ShapeNet and KITTI data sets, with different incomplete proportions and geometric shapes. Results show that the method can well supplement the missing point cloud of the target and obtain a complete, intuitive, and true point cloud model.