In the 3D point cloud semantic segmentation algorithm based on deep learning， to enhance the fine-grained ability to extract local features and learn the long-range dependencies between different local neighborhoods， a neural network based on attention mechanism and global feature optimization was proposed. First， a Single-Channel Attention （SCA） module and a Point Attention （PA） module were designed in the form of additive attention. The former strengthened the resolution of local features by adaptively adjusting the features of each point in a single channel， and the latter adjusted the importance of the single-point feature vector to suppress useless features and reduce feature redundancy. Second， a Global Feature Aggregation （GFA） module was added to aggregate local neighborhood features to capture global context information， thereby improving semantic segmentation accuracy. The experimental results show that the proposed network improves the mean Intersection?over?Union （mIoU） by 1.8 percentage points compared with RandLA-Net （Random sampling and an effective Local feature Aggregator Network） on the point cloud dataset S3DIS， and has good segmentation performance and good adaptability.