Aiming at the problem that the existing deep learning light detection and ranging (LiDAR) point cloud segmentation method ignores the relationship between high-level global single points and low-level local geometric features, which results in low point cloud segmentation accuracy, an enhanced semantic information and multi-channel feature fusion point cloud scene segmentation model is established. First, the point cloud information is supplemented, the normalized elevation, intensity value and spectral information of the point cloud are extracted to construct the multi-channel point cloud features, and the multi-scale neighborhood point cloud enhancement data set is established by using the grid resampling method. By constructing a double attention fusion layer, the feature weighted calibration in the channel dimension and feature focusing in the spatial dimension are realized, the deep information transmission of the convolution network structure is deepened, and the local regional fine-grained features of the point cloud are mined. The proposed algorithm is verified by using the data set provided by the International Association of Photogrammetry and Remote Sensing. Overall accuracy (OA) of accuracy value classification, comprehensive evaluation index (F1),and intersection ratio of the proposed algorithm, the classification results published on the association's website, and mainstream deep learning methods are compared and analyzed. Experimental results show that the proposed algorithm can achieve higher segmentation accuracy, and the average intersection union ratio on Vaihingen data set reaches 52.5%.