Tree barriers are one of the main safety threats faced by high-voltage transmission lines operating in dense mountainous areas with dense vegetation. Different tree species have different growth cycles and different risks of tree barriers within a certain period of time. In order to accurately identify the types of trees in the forest area on a large scale, this paper proposes a rapid recognition method for tree types based on airborne LiDAR measurement technology. The airborne LiDAR is used to quickly obtain point cloud data on the ground of the transmission line area, and preprocess the data to obtain the canopy point cloud of a single tree. The point cloud feature quantities of canopy spatial attribute is established, including canopy height, canopy volume, canopy point cloud density, canopy laser reflection intensity, and canopy topography features. A tree-type K-means clustering recognition model is established based on the spatial point cloud characteristics of trees, and compared with the classification results of the spectral characteristics of trees. The results show that for the trees growing in this area, the five spatial point cloud features have a good recognition effect compared to the tree species classification under the spectral characteristics. The final establishment of the K-means clustering recognition model for trees has an accuracy rate of 89% and a kappa of 0.812 for the verification data. Rapid identification of vegetation species under transmission lines is of great significance to the risk assessment and early warning of tree barriers.