The characteristics of the forest land with a canopy density of more than 60% include complex tree species,a narrow interspecific character gap,and modest interspecific distances.Traditional convolutional neural networks (CNN) are superior to other classification approaches in terms of accuracy and automation,but they also have a low learning efficiency,a tough time enhancing recognition accuracy,and poor interpretability.Additionally,the typical approach of creating maps of tree species through regular segmentation overlooks changes in tree species and boundary characteristics of remote sensing objects,making it simple to create the salt and pepper phenomenon in forest areas with dense canopies.Therefore,a Grad-Swin transformer (G-ST) based on class activation mapping and Swin transformer (ST) classification model was suggested as the solution to the aforementioned issues.It incorporates transfer learning,the ST classification model,and gradient descent class activation mapping,and by integratinglong-distance features,data enhancement,feature knowledge from other fields,and prediction training attention,it increases the G-ST classification accuracy as well as the model's generalization and interpretability abilities.Thematic maps of tree species are created by using a straightforward linear iterative clustering algorithm.The results demonstrated that the accuracy of the tree species map produced by this method was greater than that of the conventional CNN combined with regular segmentation,and the boundary between trees and remote sensing objects was closer to the vectorization result.This method can therefore serve as a useful reference for tree species image segmentation,mapping,and distribution statistics.