Automatic　segmentation　of　polyp　images　usually　results　in　low　segmentation　accuracy　due　to　the　various　sizes　of　lesion　regions　and　blurry　boundaries.　Based　on　these　two　perspectives，a　novel　Progressive　Reduction　Network（PRNet）is　proposed，which　first　locates　polyps　and　then　gradually　refines　their　boundaries.　The　network　utilizes　Res2Net　to　extract　features　from　the　lesion　region　and　leverages　the　multi-scale　cross-level　fusion　module　to　improve　localization　accuracy.　By　combining　the　attention　fusion　mechanism　with　cross-level　features　in　this　module，the　network　can　effectively　solve　the　issue　of　multi-scale　lesion　areas.　Furthermore，PRNet　combines　an　uncertain　region　processing　module　and　a　multi-scale　context-aware　module　when　restoring　image　resolution　from　top　to　bottom.　The　former　gradually　mines　polyp　edge　information　by　setting　decreasing　thresholds　to　enhance　the　recognition　of　edge　detail　features,while　the　latter,to　improve　the　overall　representation　capability　of　the　model,　further　explores　the　inherent　potential　contextual　semantics　of　lesion　regions.　In　addition,a　simple　feature　filtering　module　is　designed　in　this　algorithm　to　filter　the　valid　information　in　the　encoder　features.　Experimental　results　on　the　Kvasir-SEG,CVC-Clinic,and　ETIS　datasets　show　that　the　Dice　coefficients　of　the　algorithm　reach　92.09%,　93.05%,　and　74.19%,　respectively.　Compared　with　other　existing　polyp　segmentation　algorithms,　PRNet　outperforms　them　and　demonstrates　its　superior　robustness　and　generalization.　©　2024　Hunan　University.　All　rights　reserved.