With the rapid development of three-dimensional (3D) scanning technique, the huge volume of point cloud has been produced, which puts forward higher requirements for the performance of point cloud computing. Therefore, how to improve the efficiency of the algorithm has become a hot topic in this field. There are rich 3D shape models hidden in the ever-increasing amount of point cloud data. Inspired by the relationship between 3D shape models and the point cloud, we provide a new method to improve the execution efficiency of algorithms about point cloud computing. The 3D geometric feature analysis technology is used to obtain shape-related feature parameters, and the point cloud segmentation algorithm is proposed based on it. We use octree algorithm to organize point cloud and obtain the neighbor relationship. A self adaptive and dual linear octree algorithm is designed based on the density of point clouds to establish the data index. We build a 3D shape library by using regular shape models, and realize the algorithm for matching models with data segmentation regions. Further, we extract the shape parameters of the segmented region, which are the foundation for improving the accuracy and speed of point cloud data processing. Moreover, the segmentation effectiveness and time performance of different algorithms are compared, and the experimental results indicate that the proposed algorithm is feasible and robust.