Point cloud data can provide rich spatial information about any object or scene in the real world. Accordingly, the rapid development of the three-dimensional (3D) vision technology has promoted the point cloud data application, in which the task of performing a large-scale point cloud semantic segmentation containing millions or billions of points has received wide attention. Semantic segmentation aims to obtain the semantic class of each point, which is used to better understand 3D scenes. Driven by the common progress of the 3D scanning and deep learning technologies, point cloud semantic segmentation is being widely applied in the fields of intelligent robotics, augmented reality, and autonomous driving. First, the recent large-scale point cloud semantic segmentation methods based on deep learning are comprehensively categorized and summarized to demonstrate the latest progress in the field. Next, the commonly-used large-scale point cloud datasets and evaluation metrics for evaluating semantic segmentation models are introduced. Based on this, the semantic segmentation performances of different algorithms are compared and analyzed. Finally, the limitations of the existing methods are determined, and the future research directions for the large-scale point cloud semantic segmentation task are prospected.