In the field of point cloud processing, deep learning is a mainstream method, but the existing methods do not fully utilize the local structure information of 3D point clouds, and have less local shape perception. We proposes a 3D point cloud processing model based on improved PoinetNet. Network model introduces position adaptive convolution into PointNet. The position?adaptive convolution constructs the kernel function by combining the weight matrices in the weight bank in a dynamic way, in which the coefficients of the weight matrix are adaptively learned from the relative positions of the points through the position?relative coefficient network. The kernel function constructed in this way can better solve the problem of irregularity and disorder of point cloud data. The classification accuracy of the position?adaptive network in the 3D object classification experiment is 3.60% higher than that of PointNet, and the average intersection ratio in the 3D object part segmentation experiment is 2.20% higher than that of PointNet. In the 3D scene semantics In the segmentation experiment, the average intersection and union ratio is improved by 9.14% compared with PointNet.