Abstract: Aiming at the problems that traditional point cloud classification methods cannot extract deep features，have low automation and complex processes，based on the deep learning segmentation algo‐ rithm PointNet++ network，experimental analyses are conducted respectively on the publicly available air‐ borne LiDAR point cloud test dataset of LASDU and the self-made airborne LiDAR point cloud data of the mine. And taking OA，F1 Score and Avg F1，etc. as evaluation indicators，the analysis，comparison and evaluation with PointNet and PointCNN are carried out. The research shows that，in terms of public datasets， PointNet++ achieved the best classification results in three out of the five categories of ground objects，and the results in the other two categories are also close to the best. Moreover，in terms of overall classification performance，The OA and Avg F1 of PointNet++ have increased by 2.77，1.59 percent points and 2.32，0.87 percent points respectively compared with PointNet and PointCNN. In terms of the classification of self�made mining area feature datasets，the OA evaluation index of the PointNet++ airborne point cloud classifi‐ cation method is 71.47%，and the Avg F1 evaluation index is 61.45%，both of which are higher than those of PointNet and PointCNN，especially in the classification of ground points and buildings. Pointnet ++ has achieved the best performance in multiple indicators and has obvious advantages in self-made mining area datasets. It is suitable for complex scenarios with rich structures，can improve the accuracy of airborne point cloud classification，increase the degree of classification automation，simplify the classification process，and provide strong support for the practical application of mining area feature classification. It also provides new ideas and a foundation for the optimization of future ground object classification models and the explora‐ tion of more advanced deep learning architectures.

Key words: airborne LiDAR point cloud, complex scenes, classification model of ground objects