Abstract: In order to realize the area division of point cloud data, a segmentation algorithm (SPFCM) combining supervoxels and particle swarm optimization fuzzy C-means (PFCM) was proposed. A random sampling consensus algorithm was used to remove the point cloud plane. According to the spatial position, curvature and fast point feature histogram characteristics of the 3-D point cloud, the octree voxelization point cloud was used to obtain the supervoxel. The PFCM algorithm was used to preliminarily divide the superbody and subdivide the connected point cloud, which overcomes the shortcomings of the PFCM algorithm for stacking objects and over-segmentation of larger objects. The performance test of the SPFCM algorithm was performed on the OSD-v0.2 data set. The experimental results show that compared with the PFCM algorithm, it not only retains its advantages such as fewer parameters and simple operation, but also the index has been greatly improved, and the accuracy is up to 86%, while the recall rate reaches 83%. This research provides help and reference for the accurate segmentation of complex scenes in 3-D point clouds.