Aiming at the problem that it is difficult to extract key features clearly due to the cluttered change information of multiple feature types in different time-series remote sensing images and complex backgrounds, this paper proposes a new method of fusing Swin Transformer with twin networks to achieve building change detection. The method obtains features at different levels through the structure of four Swin Transformer blocks, and performs difference calculations for feature maps at different scales to obtain change feature maps. In addition, a difference feature fusion module and an edge-aware attention module are introduced based on the algorithm in this paper. The difference feature fusion module can better express the features under different perceptual fields and improve the fusion effect on detailed features and global features; the edge-aware attention module refines the edge features of buildings in the feature map during feature extraction, expands the local perceptual field of the model, enhances the detection ability of the model for detailed information, and thus improves the extraction ability of the network structure for building edge features. The experimental results show that the F1 values of this paper′s method are improved by 7.36% and 19.67% on two public datasets, respectively, compared with the existing classical change detection network FC-EF.