Abstract: ICESat-2 data was considered as a new land cover classification data source, and a method was proposed to classify land cover using ICESat-2 data with random forest, to explore the application potential of the space-borne photon counting lidar in the land cover classification. The method used the photon number, the proportion of horizontal and vertical distribution of different types of photons, signal-to-noise ratio, solar conditions and atmospheric conditions as the input of classification, and was verified by the experiment of multi-category land cover in China's Yangtze River Delta. For four categories of water, forest, low vegetation and urban/barren, the classification results show that the overall accuracy of strong beam and weak beam is better than 85%. For three categories of water, forest, and low vegetation/urban/barren, the classification results show that the overall accuracy of strong beam and weak beam is better than 90%.