The invention is applicable to the field of remote sensing technology, and provides an object-oriented change detection method based on multi-feature fusion, comprising the following steps of: S101, image preprocessing; S102, texture feature extraction; S103, image segmentation; S104, feature extraction of that object; S105, generating a difference image; S106, acquire an initial change detectionresult; S107, calculating feature weights; S108, the object change detection result is obtained, and each detected object is clustered into two classes of variable and invariant by fusing multi-dimensional features through weighted fuzzy C-means method. As that embodiment of the invention carry out the processing of the above step on the two-phase images, analyze and determine the weight of each dimension feature, and the weights of each dimension feature are extracted by Relief algorithm, The weighted fuzzy C-means method is formed by adding weights into the fuzzy C-means method, and the weighted fuzzy C-means method is used to fuse the multi-dimensional features, and the detection objects are clustered into two categories: variable and invariant, which effectively fuses the different features to carry out object-oriented change detection and improves the accuracy of the change detection results.