Gel　is　a　post-operative　cleaning　material　with　antibacterial　effect,　which　helps　patients　recover　after　surgery.　It　is　more　and　more　popular　in　surgery,　but　it　is　still　controversial　in　use.　This　study　collected　the　electronic　medical　records　of　patients　in　a　hospital　for　nearly　three　years,　using　a　combination　of　a　variety　of　special　selection　methods　to　process　data　and　using　random　forest,　support　vector　machine,　LightGBM　and　XGBoost　and　other　machine　learning　methods　to　predict　the　suitability　of　patients.　The　results　show　that　polysaccharide　gel　is　not　suitable　for　all　people,　whether　to　use　it　should　consider　different　situations.　This　paper　has　studied　the　applicability　of　medical　gels　to　patients,　and　established　a　predictability　model　to　provide　data　support　for　the　clinical　application　of　this　expensive　medical　material.