Point cloud is the most commonly used form of 3D data processing in the fields of autonomous driving, robotics, remote sensing, augmented reality ( AR) , virtual reality ( VR) , electric power, architecture, etc. Deep learning methods can not only handle large-scale data, but also extract features independently. Therefore, point cloud deep learning methods have gradually become a research hotspot. This article reviews the research progress of 3D point cloud analysis methods based on deep learning in the past decade. Firstly, the relevant concepts of deep learning for 3D point cloud are presented. Then, for the four tasks of point cloud object detection and tracking, classification and segmentation, registration and matching, and stitching, the principles of the corresponding deep learning methods are elaborated. Their advantages and disadvantages are analyzed and compared. Next, eighteen kinds of point cloud datasets and performance evaluation indexes for four types of point cloud analysis tasks are introduced. The performance comparison results are given. Finally, the existing problems of point cloud analysis methods are pointed out, and the further research work is prospected.