It　is　difficult　to　learn　global　remote　semantic　information　based　on　convolutional　neural　network,　and　it　is　difficult　to　obtain　multi-scale　feature　information　based　on　Vision　Transformer,　Swin　Transformer　and　Pyramid　Vision　Transformer.　However,　salient　objects　maybe　involve　different　scales.　This　paper　introduces　Shunted　Transformer　as　the　backbone　network　to　extract　multi-scale　features　to　achieve　salient　object　detection.　Aiming　at　the　problem　of　ignoring　the　difference　between　different　features　and　dilution　of　high-level　features　when　fusing　high-level　and　low-level　features,　a　decoder　for　progressive　fusion　of　multi-scale　features　is　designed.　In　addition,　to　solve　the　problem　that　the　boundary　features　obtained　may　not　match　the　salient　object　due　to　the　separation　of　the　boundary　prediction　structure　and　the　salient　object　prediction　branch,　this　paper　refers　to　the　BIG　module　and　optimizes　its　feature　input.　Finally,　the　validity　of　the　proposed　model　is　verified　by　experiments　on　four　widely　used　datasets.　©　2023　IEEE.