In the architecture, engineering, and construction (AEC) industry, point cloud semantic segmentation provides comprehensive and accurate data support for building information modeling (BIM) and is one of the key technologies for building digital twins. However, the complexity and diversity of building semantic categories and the incompleteness of the current building point cloud datasets for training make deep learning–based semantic segmentation of building point clouds still a challenging task. We systematically summarize the existing classical point cloud semantic segmentation algorithms and further compare and analyze the state-of-the-art point cloud semantic segmentation algorithms of buildings according to two application scenarios: outdoor and indoor. Second, we summarize the point cloud datasets applicable to the AEC field and quantitatively analyze and compare the semantic segmentation performance of various algorithms according to different application scenarios. Finally, we explore the research directions and application prospects of point cloud semantic segmentation algorithms in the field of AEC, encompassing data acquisition and processing, scene detection and reconstruction, digital twin, etc.