Effective acquisition of spatial structural features of point cloud data is the key to semantic segmentation of point clouds. To solve the problem that the previous methods do not make good use of global and local features， a new spatial structure feature， point box feature， is proposed for semantic segmentation. A network framework of encoding-decoding structure is designed. The global spatial and local neighborhood features of point clouds are learned by using the geometric structure feature module during the downsampling process， and the full size feature map is restored step by step in the upper sampling process for semantic segmentation. The geometric structure features module contains two sub-modules， one is the global features module， which learns the “box” features of points to represent the rough geometric features of point clouds in the sampling space. Another is the local features module， which uses feature extraction， the attention mechanism structure， to represent precise， fine-grained geometric characteristics of point clouds within local neighborhoods. Experiments are performed on the public dataset S3DIS and Semantic3D and compared with other methods. The results show that mIoU is ahead of most of the current mainstream methods， and some of the detail class IoU is the highest.