In　this　article,　we　design　this　algorithm　to　perform　the　segmentation　task　of　remote　sensing　images　by　using　attention　module　and　feature　fusion　to　deal　with　the　challenges　of　features　with　different　dimensions,　small-size　objects　and　uncertain　boundary　segmentation　in　remote　sensing　images.　The　encoder　uses　a　PVTv2　model　to　increase　the　feature　information　acquisition　capability　of　the　model,　and　several　convolutional　paths　of　the　feature　aggregation　module　can　acquire　different　size　information　of　the　image.　The　deep　network　uses　attention　mechanisms　to　obtain　features　that　are　valid　for　the　segmentation　task.　In　the　decoding　phase,　the　feature　fusion　module　fuses　the　shallow　network　detail　information　with　the　deep　network　semantic　information.　The　experimental　comparison　on　the　ISPRS　Potsdam　dataset　shows　that　pixel　accuracy　of　the　model　in　this　paper　achieves　88.59%,　IoU,　mIoU,　and　FWIoU　are　higher　than　some　classical　semantic　segmentation　networks,　which　effectively　increases　the　accuracy　of　remote　sensing　image　segmentation.　©　2023　SPIE.