The traditional tree species identification depends on timeconsuming and laborintensive efficiency of artificial field survey. In order to more effectively utilize the point cloud data identification tree of groundbased LiDAR, taking Beijing Forestry University as the research area, and FARO Photon 120 groundbased LiDAR was used to obtain point cloud data of a sample set of 92 trees, four tree species in the study area. According to the threedimensional coordinate values of point cloud, the six treemeasuring factors of breast diameter, height of branches, height of tree, height of crown, width of crown, and the longest direction of vertical trees in the study area were extracted, and the extracted treemeasuring factors were combined. The robust tree features six parameters, namely crown length tree height ratio, DBH height ratio, crown height tree height ratio, branch angle, crown length ratio, maximum crown width and vertical direction. For the ratio of crown width, the tree species were automatically identified by using the treemeasuring factor and the combined feature point parameters to support the tree sample by using the support vector machine, the classification regression decision tree and the random forest. The results showed that for the tree identification method using treemeasuring factor, the average accuracy of recognition was 0.765, and the average recall rate was 0.778. Among the three identification methods, the best effect was classification regression decision, followed by random forest, and finally support vector. Using the combined feature parameter tree identification method, the average accuracy of recognition was 0.891, and the average recall rate was 0.896. The best method was random forest and support vector machine, followed by classification regression decision. In general, the combined feature parameter method had higher accuracy and recall rate of single tree species or overall than those of the treemeasuring factor method, random forests were relatively the best for three different classification methods. The research result showed that the tree species identification classification combining the point cloud obtained by ground-based LiDAR and different machine learning classification methods could achieve satisfactory results and save a lot of time and manpower.