A remote sensing image segmentation network called AFSM-Net, which combines a feature map segmentation module and an attention mechanism module, is proposed to address the issues of low recognition and low segmentation accuracy of small targets in remote sensing image segmentation using conventional convolutional neural networks. First, the feature map segmentation module is introduced in the coding stage to enlarge each segmented feature map and extract features by sharing parameters; then, the extracted features are fused with the original output image of the network; and finally, the attention mechanism module is introduced into the network model to make it pay more attention to the effective feature information in the image and ignore the irrelevant background information, to improve the feature extraction ability of the model for small target objects. The experimental results show that the average intersection ratio of the proposed method is 86.42%, which is 3.94 percentage points higher than that of the DeepLabV3+ model. The proposed method fully considers the attention of small and medium targets in image segmentation, and improves the segmentation accuracy of remote sensing images.