It　is　important　for　aquaculture　monitoring,　scientific　planning,　and　management　to　extract　offshore　aquaculture　areas　from　medium-resolution　remote　sensing　images.　However,　in　medium-resolution　images,　the　spectral　characteristics　of　offshore　aquaculture　areas　are　complex,　and　the　offshore　land　and　seawater　seriously　interfere　with　the　extraction　of　offshore　aquaculture　areas.　On　the　other　hand,　in　medium-resolution　images,　due　to　the　relatively　low　image　resolution,　the　boundaries　between　breeding　areas　are　relatively　fuzzy　and　are　more　likely　to　＇adhere＇　to　each　other.　An　improved　U-Net　model,　including,　in　particular,　an　atrous　spatial　pyramid　pooling　(ASPP)　structure　and　an　up-sampling　structure,　is　proposed　for　offshore　aquaculture　area　extraction　in　this　paper.　The　improved　ASPP　structure　and　up-sampling　structure　can　better　mine　semantic　information　and　location　information,　overcome　the　interference　of　other　information　in　the　image,　and　reduce　＇adhesion＇.　Based　on　the　northeast　coast　of　Fujian　Province　Sentinel-2　Multispectral　Scan　Imaging　(MSI)　image　data,　the　offshore　aquaculture　area　extraction　was　studied.　Based　on　the　improved　U-Net　model,　the　F1　score　and　Mean　Intersection　over　Union　(MIoU)　of　the　classification　results　were　83.75%　and　73.75%,　respectively.　The　results　show　that,　compared　with　several　common　classification　methods,　the　improved　U-Net　model　has　a　better　performance.　This　also　shows　that　the　improved　U-Net　model　can　significantly　overcome　the　interference　of　irrelevant　information,　identify　aquaculture　areas,　and　significantly　reduce　edge　adhesion　of　aquaculture　areas.