Aiming at the problems of edge feature recovery, high-dimensional feature extraction and context information fusion in remote sensing image segmentation, this paper proposes an improved multi-scale high-dimensional feature fusion remote sensing image semantic segmentation network (MHS-UNet). Based on U-Net network, the multi-branch convolutional attention mechanism and high-dimensional feature extraction module are introduced into the encoder, which effectively enhances the multi-level capture of remote sensing image features. The dynamic upsampling feature fusion method is used in the decoder to avoid the information loss caused by feature fusion, and to improve the fusion effect of shallow and deep features. Comparison and ablation experiments were carried out on LoveDA and WHDLD datasets. The results showed that the mIoU of MHS-UNet on the two datasets reached 58.60% and 63.67%, respectively, which effectively improved the semantic segmentation accuracy of remote sensing images.