Abstract: For mobile LiDAR point cloud data, a new hybrid index structure combining global KD-tree and local Octree is proposed to improve the efficiency of data organization and management, which is named as KD-OcTree index. Firstly, global KD-tree reconstructs the spatial neighborhood relations by defining the segmenting dimension and segmenting planes, for the purpose of ensuring the balance of the whole index. Then, local Octree is constructed in the leafs of KD-tree, which can avoid some shortcomings such as the unbalance of point cloud distribution, deeper Octree, large amount of non-point space, and so on. Lastly, we take three real scenes' point clouds as test data to process. The experimental results and comparative analysis show that the KD-OcTree index can not only improve the speed of constructing index and neighborhood searching, but also improve the effect of data-processing and influence the reliability of classification.