The　intestinal　polyp　is　one　of　the　common　intestinal　diseases,　which　is　characterized　by　protruding　lining　tissue　of　the　colon　or　rectum.　Considering　that　they　may　become　cancerous,　they　should　be　removed　by　surgery　as　soon　as　possible.　In　the　past,　it　took　a　lot　of　manpower　and　time　to　identify　and　diagnose　intestinal　polyps,　which　greatly　affected　the　treatment　efficiency　of　medical　staff.　Because　the　polyp　part　looks　similar　to　the　normal　structure　of　the　human　body,　the　probability　of　human　eye　misjudgment　is　high.　Therefore,　it　is　necessary　to　use　advanced　computer　technology　to　segment　the　intestinal　polyp　image.　In　the　model　established　in　this　paper,　an　image　segmentation　method　based　on　convolution　neural　network　is　proposed.　The　Har-DNet　backbone　network　is　used　as　the　encoder　in　the　model,　and　its　feature　processing　results　are　converted　into　three　feature　images　of　different　sizes,　which　are　input　to　the　decoding　module.　In　the　decoding　process,　each　output　first　expands　the　receptive　field　module　and　then　fuses　the　feature　image　processed　by　the　attention　mechanism.　The　fusion　results　are　input　to　the　density　aggregation　module　for　processing　to　improve　the　operation　efficiency　and　accuracy　of　the　model.　The　experimental　results　show　that　compared　with　the　previous　Pra-Net　model　and　Har-DNet　MSEG　model,　the　accuracy　and　precision　of　this　method　are　greatly　improved,　and　can　be　applied　to　the　actual　medical　image　recognition　process,　thus　improving　the　treatment　efficiency　of　patients.　©　2023,　International　Journal　of　Advanced　Computer　Science　and　Applications.　All　Rights　Reserved.