Machine vision is one of the important measure manners for environmental perception. It is a research hotspot in the fields of automatic driving, robot, industrial detection and so on. The fine analysis of point cloud data is one of the key technologies. To solve the problem of low segmentation accuracy of large-scale point cloud data of real scene, a bilateral feature aggregation network architecture for semantic segmentation of the point cloud is proposed. Firstly, a bilateral feature aggregation module is formulated to aggregate local features by processing the geometric information and semantic information of the point cloud. The aim is to make full use of the feature information of the point cloud. Secondly, the high-dimensional spatial correlation of nearest neighbor features is used to calculate the impact between points. The context information of local neighborhood is enhanced. A hybrid-pooling architecture is proposed to replace the max-pooling to reduce the information loss of max-pooling, and the horizontal skip connection pooling is used to enhance feature diversity. Finally, an attention module is introduced to extract global features, which can filter scale noise and enhance the spatial expressiveness of features. Experimental results show that the mean intersection over union of the proposed method is 68. 2% , and the mean accuracy is 80. 7% . These two values are 20. 6% and 14. 5% higher than those of the PointNet. The objective indicator is better than the existing representative methods.