The laser point cloud is the output of a 3D sensor,and its semantic segmentation task is fundamental to understanding the real world. The point cloud segmentation network based on graph convolution shows excellent performance in many scenarios.However,existing graph convolution methods are partially problematic:the local representation of the point cloud is not augmented,global geometric information is ignored,and the aggregation operation only keeps the local maximum response value information,while the sub maximum information is lost. In order to address these problems,the GRes Net network is proposed in this paper. The Local Geometry Augment(LGA) module is used to make the networkrotationally invariant to the Z axis,so as to strengthen the local information representation of the point cloud. The Global Geometry Feature (GGF) module is used to calculate the volume ratio between local and global spheres,and connect it with coordinate feature X,so that the global geometry information feature can be retained. Multiple symmetric aggregation operations are used to preserve the local information in many ways. Residual operations are used in each layer of the network to transfer information from one layer to the next as well as to mine deeper semantic information using the Reversed Residual MLP (RevResMLP) module. In this paper,semantic scene segmentation is conducted on S3DIS data set to verify the performance of network segmentation.The experimental results show that the segmentation accuracy of this method is up to 61%,which is 14% higher than that of the benchmark network DGCNN,and the performance of the model is effectively improved.