The use of remote sensing technology to extract the composition and distribution of wetland vegetation communities is of great significance to the construction of wetlands.Taking Qinghai Ulandulan Lake National Wetland Park as the research area,using the number of Jilin No.1 remote sensing images,KNN and RF classification models were selected through image segmentation and feature optimization,the vegetation community of Dulan Lake wetland was divided,and the classification accuracy was verified.The results showed that according to the segmentation scale provided by ESP 2 tool,the optimal segmentation scale for object-oriented classification of vegetation communities was 18,and the segmentation scales of vegetation and non-vegetation areas were 32 and 85,respectively.For character type division,only using the threshold of image band information and related index could not accurately extract the feature category,it was necessary to combine the image geometric features and texture features to improve the classification accuracy,use the feature space optimization tool to optimize 61 image features,and finally screen out 40 image features,and use them for classification.According to the confusion matrix classification accuracy evaluation results,the classification results of KNN algorithm were better than RF,among which the overall classification accuracy of KNN was 81.80%,the Kappa coefficient was 0.79,the overall classification accuracy of RF was 72.59%,and the Kappa coefficient was 0.68.According to the classification results,the vegetation coverage rate of Dulan Lake wetland was 44.41%,and the composition and distribution characteristics of vegetation communities in the results could provide a basis for wetland ecological construction and management.