Under complex background, the characteristics of buildings at different scales are quite different, and the existing algorithms have problems such as uneven segmentation and misjudgment of multiscale building segmentation. To solve the above problems, we design a new network structure adapted to scale changes. Firstly, aiming at the problem of low segmentation accuracy in remote sensing image scenes, we introduce and embed a coordinate attention mechanism in the basic network to enhance the context information capture ability, eliminate noise and enhance the network′s ability to extract spatial features. we introduce A new recursive residual convolution module ed to deepen the network layer, reduce information loss, and improve the efficiency of feature extraction. Finally, we introduce a hollow space convolutional pooled pyramid in the hop connection to increase the network receptive field, enhance the effective features, and suppress the useless features. Design the system to verify the usefulness of the model. Experimental results show that the proposed method improves the accuracy, recall, F1score, and IoU indicators by 305%, 156%, 13%, and 308% compared with the UNet network, respectively.