The　semantic　segmentation　task　of　medical　image　is　to　segment　the　focus,　organ　or　substructure　of　human　body　in　medical　image.　It　plays　an　important　role　in　locating　and　identifying　the　diseased　area　and　making　medical　plan.　In　various　medical　image　segmentation　tasks,　the　U-shaped　architecture　has　achieved　great　success.　Transunet　introduces　Transformer　with　global　attention　mechanism　into　the　U-shaped　architecture,　which　overcomes　the　inherent　limitations　of　convolution,　but　because　it　still　continues　the　original　skip　connections　structure,　it　will　bring　the　strong　noise　from　features　in　the　shallow　network　into　the　high　semantic　features　of　the　deep　network,　thus　affecting　the　segmentation　accuracy.　UTSN-net　model　based　on　the　combination　of　Transformer　and　nonlocal　attention　mechanism　is　proposed.　UTSN-net　uses　Transformer　to　overcome　the　inherent　limitations　of　convolution,　and　introduces　the　skip　connections　module　based　on　nonlocal　attention　mechanism　into　the　U-shaped　network,　which　can　comprehensively　consider　the　deep　features　with　global　context　information　and　the　shallow　features　with　accurate　high-resolution　positioning　information　to　improve　the　accuracy　of　segmentation　results.　Experiments　on　synapse　multi-organ　abdominal　CT　dataset　verify　that　UTSN-net　has　better　semantic　segmentation　performance.　©　2023　SPIE.