With the rapid development of 3D vision, large-scale 3D point cloud processing in real time based on deep learning has become a research hotspot. Taking a large-scale 3D point cloud with disordered spatial distribution as the background, this study comprehensively analyzes, introduces and compares the latest progress of deep learning in real-time processing of 3D vision problems. Then, it analyzes in detail and compares the advantages and disadvantages of algorithms in terms of point cloud segmentation, shape classification and target detection. Further, it briefly introduces the common data sets of point clouds and compares the algorithm performance of different data sets. Finally, the study points out the future research direction of 3D point cloud processing based on deep learning.