Aquaculture　has　experienced　significant　growth,　contributing　to　resolving　the　global　food　crisis　and　delivering　substantial　economic　benefits.　Nevertheless,　the　uncontrolled　expansion　of　aquaculture　activities　has　led　to　an　ecological　crisis　in　offshore　waters.　This　highlights　the　critical　need　for　precise　delineation　and　monitoring　of　aquaculture　areas　in　these　regions　to　ensure　scientific　management　and　sustainable　development　of　coastal　areas.　In　this　paper,　we　introduced　a　SRUNet　model　based　on　the　Swin　Transformer　for　accurately　extracting　offshore　raft　aquaculture　areas　using　medium　resolution　remote　sensing　images.　Our　SRUNet　model　combined　the　UNet　model　with　the　Swin　Transformer　block　and　the　Residual　block　to　account　for　multiscale　features,　resulting　in　excellent　extraction　performance　in　diverse　and　complex　sea　areas.　To　evaluate　the　model,　we　selected　four　typical　raft　aquaculture　areas　and　compared　the　SRUNet　model　with　other　comparative　network　models.　Results　revealed　that　the　SRUNet　model　outperformed　all　other　models,　and　the　F1　Score　and　MIoU　of　the　classification　results　were　86.52%　and　87.22%,　respectively.　The　model　reduced　the　loss　of　feature　information　and　misclassification　of　aquaculture　areas,　generating　extraction　effects　that　aligned　closely　with　real　aquaculture　area　shapes.　Additionally,　we　tested　the　performance　of　each　component　of　the　SRUNet　model.　The　results　indicate　that　the　SRUNet　model　exhibits　strong　robustness,　and　effectively　filters　out　irrelevant　information.　These　results　demonstrate　the　model's　potential　for　large-scale　extraction　of　offshore　aquaculture　areas.　Author