Hyperspectral remote sensing data provides the possibility for fine identification of tree species. In order to explore the ability of hyperspectral data in tree species identification, this study is based on the leaf hyperspectral data of eight major tree species in the heritiera littoralis community of Baguang, Shenzhen, and compared the performance of six spectral preprocessing methods and two classification methods to classify tree species. Then based on the random forest algorithm, the importance of the each band was evaluated. The results showed that the first derivative preprocessing method had the best performance in classification and identification, and the average classification accuracy was 76.65%. The random forest regression method had better performance than the support vector regression algorithm, and the model average classification recognition accuracy was 73.07%. It can be seen from the confusion matrix that Aidia pycnantha, Aporosa dioica, Cinnamomum burmanni were recoginized as Sterculia lanceolato. There were the misclassification between Scheffero octorphylla and aporosa diocia. And Heritiera littoralis was also misclassified as Ficus microcarpa. Spectral data near 400 nm, 495 nm, 615-675 nm, 835 nm, 915-975 nm, 1035-1065 nm, 1085-1135 nm, 1265-1275 nm, 1425-1535 nm, 2040 nm, 2100-2270 nm, and 2430 nm are identified as the spectral features, which are most important for the classification of eight tree species.