Hyperspectral laser radar combines the characteristics of LiDAR and hyperspectral information, and provides more accurate remote sensing detection methods of the extraction of vegetation physiological and biochemical parameters, but its application potential has not been fully explored. In this paper, the leaves of 10 typical tree species in Beijing are taken as samples to carry out the leaf observation experiment of indoor hyperspectral laser radar. And the tree species classification research is carried out to provide the basis of the future forestry application of hyperspectral laser radar. In this study, the hyperspectral data of tunable hyperspectral LiDAR (HSL) was carried out and compared with the data measured by ASD spectrometer. Secondly, 10 kinds of leaves were classified by random forest method. In the process, the total spectral index is obtained by combining all the bands and some sensitive bands with the spectral index. The results show that: (a) HSL is consistent with ASD spectra observed in the band 650~1 000 nm (71 channels) (R²=0.9525~0.993 2, RMSE=0.058 7); (b) The classification accuracy of the original band reflectivity is 78.31%, there into the maximum contribution rate of the classification band is 650~750 nm, and the classification accuracy is 94.18% using this band which shows that it is very effective in classify tree species by using red edge band (650~750 nm); (c) the bands sensitive to tree species classification are 680 nm, 685 nm, 690 nm, 715 nm, 720 nm, 725 nm, 730 nm; (d) When we combine the spectral index and vegetation index, the classification accuracy is 82.65%. This study shows that at the single leaf level, hyperspectral LiDAR can accurately reflect the spectral characteristics of the target leaves and classify the species of different trees effectively. It is possible to extract physiological and biochemical parameters of targets for the future field applications accurately.