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The effectiveness of airborne LiDAR point cloud and aerial imagery on tree species classification and the effect of XGBoost algorithm for feature selection on tree species classification accuracy were researched, and the ability three non-parametric classifiers of random forest, support vector machine and artificial neural network to classify tree species on a single-wood scale were evaluated. Aiming at the current background effect of canopy extraction and the problem of over-segmentation, the traditional single tree canopy segmentation method was improved by using the visible light difference vegetation index and bilateral filtering;and then the single tree canopy was used as an object to extract multi-dimensional features by using the XGBoost algorithm to perform feature importance ranking and feature selection. Finally, three non-parameter classifiers of random forest, support vector machine and artificial neural network were used to design 12 classification schemes to classify single tree species and do accuracy evaluation. The results showed that the improved single tree segmentation method can effectively improve the accuracy of tree crown extraction, and the accuracy of the obtained tree canopy segmentation results was more than 80%;the LiDAR data and aerial orthophotos were combined, and the ANN classifier was used for feature selection after XGBoost algorithm for feature selection. The scheme had the highest accuracy, with an overall accuracy of 86.19%, indicating that multi-source data synergy and feature selection can improve the accuracy of tree species classification. The ANN classifier had the strongest ability to classify existing tree species on a single tree scale.