Semantic segmentation of 3D point clouds is a key technology in the fields of autonomous driving and computer vision. Deep learning-based semantic segmentation models suffer from inefficient operation due to their complex network structures. To address the inefficiency of deep neural networks in 3D point cloud semantic segmentation， Point-MLPBLS， a point cloud semantic segmentation network based on Multi-Layer Perceptron （MLP） cascaded Broad Learning System （BLS） was proposed. First， the overall network adopted a dual network collaborative architecture： a feature extraction network and a point cloud segmentation network. Second， the feature extraction network performed a sampling-grouping-aggregation operation to the input point cloud data， and an inverted residual module was added to enhance the local feature representation capability. Third， a feature propagation module was used to achieve upsampling， thereby restoring the original point cloud resolution and enabling multi-scale feature fusion， thereby constructing a refined point cloud representation. Finally， the point cloud segmentation network replaced deep iterative training with a flattened MLP cascade structure， and spatially-aware MLPs were embedded in the feature mapping layer， so as to achieve high-precision inference. Experimental results show that Point-MLPBLS improves the mean Intersection-over-Union （mIoU） by 12.9 percentage points and reduces the segmentation time by 41.1% compared with PointNet++ on the S3DIS dataset， improving the efficiency of point cloud segmentation， and providing an efficient solution for 3D point cloud semantic segmentation.