When the K-means algorithm is used for single tree recognition for the forest point cloud data collected by the current Lidar (Light Detection And Ranging,LIDAR),the algorithm has a long convergence time and is prone to clustering for forest scenes with high density of trees. An improved multi-layer K-means single tree recognition algorithm was proposed.Taking the point cloud data of the Larix olgensis plantation in Mengjiagang Forest Farm in Jiamusi City,Heilongjiang Province as the experimental object,the RANSAC algorithm and radius outlier denoising algorithm were used to remove ground points and non-trunk and non-ground points in the data.Finally,the single tree recognition was carried out through the multi-layer K-means algorithm.The results showed that the improved multi-layer K-means algorithm had a single tree recognition accuracy rate of 91.01% and the false calculation amount of the trees was 0.Compared with the traditional K-means algorithm,the convergence time of the algorithm was shortened by 48.13%.It can be concluded that the multi-layer K-means algorithm is more efficient,and single tree identification in complex and dense forest plots has better results.The cost of surveying forest structure is reduced,which is of great significance to the calculation of forest structure parameters,the protection of forest resources and the overall planning.