To improve the accuracy of semantic segmentation for remote sensing images and address the loss problem of small-sized target information during feature extraction by Deep Convolutional Neural Network （DCNN）， a semantic segmentation method based on multi-scale feature fusion named FuseSwin was proposed. Firstly， an Attention Enhancement Module （AEM） was introduced in the Swin Transformer to highlight the target area and suppress background noise. Secondly， the Feature Pyramid Network （FPN） was used to fuse the detailed information and high-level semantic information of the multi-scale features to complement the features of the target. Finally， the Atrous Spatial Pyramid Pooling （ASPP） module was used to capture the contextual information of the target from the fused feature map and further improve the model segmentation accuracy. Experimental results demonstrate that the proposed method outperforms current mainstream segmentation methods.The mean Pixel Accuracy （mPA） and mean Intersection over Union （mIoU） of the proposed method on Potsdam remote sensing dataset are 2.34 and 3.23 percentage points higher than those of DeepLabV3 method， and 1.28 and 1.75 percentage points higher than those of SegFormer method. Additionally， the proposed method was applied to identify and segment oyster rafts in high-resolution remote sensing images of the Maowei Sea in Qinzhou， Guangxi， and achieved Pixel Accuracy （PA） and Intersection over Union （IoU） of 96.21% and 91.70%， respectively.