Abstract: Aiming at solving the problem of low efficiency of point cloud classification caused by massive nodes and undirected edges when using traditional high-order conditional random field model,a point cloud classification method based on multi-scale voxel and high order random fields is proposed.Firstly,multiscale voxel is utilized as a node of undirected graph to replace the mass of discrete point clouds and reduce the number of nodes and undirected edges.Then,the supervoxel segmentation result is used as a higher-order cluster based on which an unsupervised distributed spatial context is designed as higher-order cluster eigenvector to improve the classification result.Finally,combined with the constructed graph model and each order eigenvector,classical high-order conditional random field model is implemented for automatic point cloud data classification.The Oakland standard dataset is used as the experimental data.Experimental results show that the classification efficiency of the high-order conditional random field point cloud classification model is improved by 5 to 10 times under the premise of ensuring the classification accuracy.