Taking point cloud data as research object, this paper proposes a hybrid octree mixing point cloud index structure which combines a K-dimensional tree (KD-tree) spatial segmentation idea, and realizes efficient management of mass point cloud. In this paper, the space of the point cloud is first divided by the KD-tree idea. On this basis, octree is used for further segmentation to establish an octree-like index structure. Then, in order to achieve better spatial management and neighborhood search, the traditional linear octree coding is improved and optimized. Finally, using five groups of incremented point cloud set as test data, experimental results and comparison analysis show that the octree can make the overall structure of the data organization more reasonable, effectively improve access efficiency, and reduce the memory space. The index structure not only improves the speed of the traditional KD tree construction index but also improves the problem that the traditional octree takes too much space and the neighborhood search takes too long. It achieves reasonable management of massive point cloud space.