In order to solve the problem of inefficient querying arising from irregular and uneven distribution of point cloud data,a multi level index structure based on the spatial distribution characteristics of 3D point cloud data is proposed. The point cloud spatial information is introduced into the traditional octree to form a new data structure the oriented octree,which is used for the global division of point cloud space. Before each division of the space,a principal component analysis is performed on the point cloud data to form an oriented bounding box of the nodes,and then the space is further divided into eight subspaces. To achieve fast scheduling and querying of data,KD tree is used for the secondary organization and construction of the data in the leaf nodes locally. The experimental results show that the oriented octree can effectively reduce the total number of nodes and the number of redundant nodes,the combined nested structure of oriented octree and KD tree can effectively divide the huge amount of point cloud data,realize efficient retrieval of point cloud data,and effectively manage point cloud data.