Abstract: Airborne hyperspectral data can reflect the spectral characteristics of tree species, which can be used for precise classification of forest tree species. This study applies different machine-learning classification algorithms to classify tree species in hyperspectral images of unmanned aerial vehicles（UAV）. Firstly, a UAV was used to collect hyperspectral data from the Maor Mountain Experimental Forest Farm in Heilongjiang Province, and a series of preprocessing was completed for the obtained data. Then, three different machine learning classification algorithms, namely, support vector machine（SVM） based on Gaussian kernel, random forest（RF）, and K-nearest neighbor（KNN）, were used to establish the tree species classification models, respectively, based on the fullband hyperspectral data. Meanwhile, tree species classification models were constructed based on the dimension-reduced full-band hyperspectral data using different band selection methods（successive projections algorithm, competitive adaptive reweighted sampling method and uninformative variable elimination method）. Finally, the tree species classification model was constructed by combining different band selection methods and hyperspectral image texture features, and the results of different processing methods were compared. Research shows, the kernel SVM had the highest classification accuracy（87. 55%） among the tree species classification models with full-band hyperspectral data. After selecting different bands, the stability of RF is the best among the three classification algorithms, and the accuracy rate was high, while the classification accuracy of the support vector machine based on the Gaussian kernel improved with the increase of feature dimension. The accuracy of the tree species classification model established by extracting texture features based on a grayscale co-occurrence matrix combined with band selection was higher than that of the model established by a single band selection. In particular, the K-nearest neighbor classification algorithm has the greatest improvement, which indicated that modeling with clearly partitioned features can achieve good classification results. This study used different feature selection methods combined with three different machine learning classification algorithms to achieve dominant tree species classification based on hyperspectral data, which provides technical reference for the combination of band selection methods and machine learning algorithms, and it is also of great significance for forest biomass retrieval and carbon storage estimation based on UAV hyperspectral data.