Abstract: In order to explore the optimal feature combination of Sentinel-2 remote sensing image stand type classification, the classification and effect evaluation of broad-leaved forest, masson pine forest, fir forest and bamboo forest were realized. Selecting Changting County, Fujian Province as the study area, 10 original bands（O） were extracted from Sentinel-2 images, and 9 spectral indices（S）, 7 red-edge spectral indices（R）, 8 texture features（Te） were calculated, and 2 terrain feature indices（To） based on digital elevation data were calculated, for a total of 36 features. Using random forest algorithm to analyze the importance of different features in stand type classification, using out of band（OOB） data and average impurity reduction method to optimum individuality combination（OIC）. 6 different experimental protocols（O, O+To, O+To+S, O+To+S+R, O+To+S+R+Te, and OIC） were classified into stand types and the results were evaluated by confusion matrix. The results showed that the importance of the 36 features involved in the classification of stand types was 2.11%-5.43%, the altitude factor was the most important, and the mean and correlation of the red edge band, red edge spectral index, and texture features were also of high importance. Using the original band alone to classify the stand types, the classification accuracy was not high, the overall accuracy was 73.26%, and the Kappa coefficient was 0.64. Based on the original band, other features were introduced. Except the original band, other features can improve the classification accuracy. The optimum individuality combination（OIC） was the top 27 features of importance, including altitude, 8 original bands, 7 red-edge spectral indices, and 3 texture features, the classification accuracy was the highest, the overall accuracy was 83.13%, and the Kappa coefficient was 0.77, which was 0.82%-9.87% higher than the overall classification accuracy of the other five experimental schemes. Using Sentinel-2 images as the data source, the feature combination optimized by the random forest algorithm integrated the features that had an important contribution to the classification of stand types among the multi-type features, thereby improving the classification accuracy. The research results can provide reference for GEE platform Sentinel-2 image extraction of stand type information in forest resource survey.