The invention relates to a high-resolution remote sensing image city function partitioning method based on multi-feature fusion. The method comprises the steps of 1, image preprocessing; step 2, distributing the feature values in each image to the visual word most similar to the feature value, and counting the corresponding word frequency of each visual word to form a visual word feature; constructing a multi-feature BoW visual dictionary; 3, constructing an LDA probability topic model, and mining a high-dimensional semantic vector of the image by utilizing the LDA probability topic model; 4,training an SVM classifier according to the high-dimensional semantic vector obtained in the step 3; and step 5, carrying out urban function partitioning on the test set by using an SVM classifier. The method has the beneficial effects that the POI data is introduced, so that the wrong score of the remote sensing data caused by metamerism and foreign matters in the same spectrum is reduced; multiple features of the image, which comprises local features, spectral features, texture features, surface temperature features, spatial three-dimensional features and POI features, are comprehensively utilized, and higher classification precision can be obtained under the condition that the single feature of the image is not obvious.