In　order　to　explore　the　important　factors　affecting　the　structural　safety　of　houses　in　urban　waterlogged　areas,　21　attributes,　such　as　house　year,　floor　and　area,　were　collected　and　selected　to　construct　an　early　warning　index　system,　and　the　discretization　and　imbalance　of　samples　were　solved　by　over-sampling　and　unique　heat　coding.　Secondly,　four　different　integrated　algorithms　and　six　machine　learning　models　were　used　to　build　an　early　warning　model　to　learn　and　test　the　safety　data　of　housing　structures.　Then,　the　performance　of　the　early　warning　model　was　compared　comprehensively　by　applying　the　harmonic　average　of　accuracy,　accuracy　and　recall,　average　accuracy　and　area　under　the　curve　(AUC),　and　the　correlation　analysis　and　importance　ranking　of　each　warning　index　were　carried　out.　Finally,　2　215　houses　in　35　waterlogging　areas　in　11　counties　and　cities　of　Fujian　Province　were　taken　as　examples　to　verify　the　scientific　and　validity　of　the　model.　The　results　show　that:　whether　the　house　belongs　to　the　key　inspection,　whether　the　construction　team　is　professional,　house　area,　year　and　the　number　of　the　ground　floor　are　all　more　than　150,　which　are　the　most　important　five　indicators　for　building　safety　warnings　in　vulnerable　waterlogging　areas.　The　early-warning　model　based　on　the　lifting　method　strategy　has　the　best　early-warning　accuracy,　and　the　overall　prediction　accuracy　is　99.　10%.　The　model　can　detect　the　structural　safety　of　houses　in　vulnerable　waterlogging　areas　more　accurately　and　efficiently.　©　2023　Fine　Chemicals.　All　rights　reserved.