It　is　challenging　to　directly　obtain　global　information　of　existing　deep　learning-based　remote　sensing　intelligent　interpretation　methods,　resulting　in　blurred　object　edges　and　low　classification　accuracy　between　similar　classes.　This　study　proposes　a　semantic　segmentation　model　called　SRAU-Net　based　on　Swin　Transformer　and　convolutional　neural　network.　SRAU-Net　adopts　a　Swin　Transformer　encoder-decoder　framework　with　a　U-Net　shape　and　introduces　several　improvements　to　address　the　limitations　of　previous　methods.　First,　Swin　Transformer　and　convolutional　neural　network　are　used　to　construct　a　dual-branch　encoder,　which　effectively　captures　spatial　details　with　different　scales　and　complements　the　context　features,　resulting　in　higher　classification　accuracy　and　sharper　object　edges.　Second,　a　feature　fusion　module　is　designed　as　a　bridge　for　the　dual-branch　encoder.　This　module　efficiently　fuses　global　and　local　features　in　channel　and　spatial　dimensions,　improving　the　segmentation　accuracy　for　small　target　objects.　Moreover,　the　proposed　SRAU-Net　model　incorporates　a　feature　enhancement　module　that　utilizes　attention　mechanisms　to　adaptively　fuse　features　from　the　encoder　and　decoder　and　enhances　the　aggregation　of　spatial　and　semantic　features,　further　improving　the　ability　of　the　model　to　extract　features　from　remote　sensing　images.　The　effectiveness　of　the　proposed　SRAU-Net　model　is　demonstrated　using　the　ISPRS　Vaihingen　dataset　for　land　cover　classification.　The　results　show　that　SRAU-Net　outperforms　other　models　in　terms　of　overall　accuracy　and　F1　score,　achieving　92.06%　and　86.90%,　respectively.　Notably,　the　SRAU-Net　model　excels　in　extracting　object　edge　information　and　accurately　classifying　small-scale　regions,　with　an　improvement　of　2.57　percentage　points　in　the　overall　classification　accuracy　compared　with　the　original　model.　Furthermore,　it　effectively　distinguishes　remote　sensing　objects　with　similar　characteristics,　such　as　trees　and　low　vegetation.