Aiming at the problems of unclear segmentation targets, unclear edges, and ineffective fusion of global features and edge features in point cloud segmentation, a 3D point cloud semantic segmentation algorithm fused with edge detection was proposed. First, the global semantic features in the region are initially extracted from the point cloud data through the 3D point cloud semantic segmentation network. Then, the semantic edge detection network with the introduction of an attention mechanism is adopted, which can better extract and enhance the features of objects in the point cloud data, suppress the generation of non-edge information, and obtain edge features with rich semantic information. Finally, the semantic features belonging to the same object are fused by the fusion module for segmentation and refinement processing, which makes the segmentation target more accurate. In addition, dual semantic loss functions are used, which enables the network to produce semantic segmentation results with better boundaries. By building an experimental platform and using the S3DIS standard data set to test, the average interaction ratio of the improved algorithm on the data set is 70. 21%, which is better than the KPConv semantic segmentation algorithm in precision. The experimental results show that the algorithm can effectively solve the problems of unclear boundary segmentation and fuzzy edge information, and the overall segmentation performance is good.