Over the recent years, the popularity of depth sensors and three-dimensional(3D) scanners has enabled the rapid development of 3D point clouds. As a key step in understanding and analyzing three-dimensional scenes, semantic segmentation of point clouds has received extensive research attention. Point cloud semantic segmentation based on deep learning has become a current research hotspot owing to the excellent high-level semantic understanding ability of deep learning. This paper briefly discusses the concept of semantic segmentation, followed by the advantages and challenges of point cloud semantic segmentation. Then, the point cloud segmentation algorithms and common datasets are introduced in detail. This paper also summarizes the deep learning methods based on point ordering, feature fusion, and graph convolutional neural network in the field of point cloud semantic segmentation. Finally, it analyzes the quantitative results of proposed methods and forecasts the development trend of point cloud semantic segmentation technology in the future.