In order to mine and sense the geometric features of point clouds and further improve the semantic segmentation effect of point clouds by feature enhancement， a point clouds semantic segmentation network based on feature enhancement was proposed. Firstly， the Geometric Feature Sensing Of Point cloud （GFSOP） module was designed to make the network capable of sensing the local geometric structure of point clouds， semantic representations were enhanced by capturing spatial features between points， and multi-scale features were obtained by the idea of hierarchical extraction of features. At the same time， spatial attention and channel attention were fuseed to predict semantic labels of point clouds， and the segmentation performance was improved by strengthening spatial correlation and channel dependence. Experimental results on the indoor dataset S3DIS （Stanford large-scale 3D Indoor Spaces） show that compared with PointNet++， the proposed network improves the mean Intersection over Union （mIoU） by 5.7 percentage points and the Overall Accuracy （OA） by 3.1 percentage points， and has stronger generalization performance and more robust segmentation effect on point clouds with problems of noise， uneven point cloud density and unclear boundaries.