Water　quality　assessment　model　and　spatiotemporal　heterogeneity　pose　challenges　to　the　uncertainty　of　water　quality　assessment.　To　improve　the　accuracy　of　the　water　quality　index　(WQI)　model,　multiple　machine　learning　algorithms　(CatBoost,　SVM,　LR,　XGBoost,　LightGBM)　and　entropy　weight　method　(EWM)　were　introduced　to　determine　the　objective　weight.　Six　combined　weights　were　determined　by　game　theory　combining　objective　and　subjective　weights　(AHP).　Three　aggregation　functions　were　established,　including　a　new　function　proposed　based　on　the　sigmoid　function　and　two　existing　functions.　Based　on　the　six　combined　weights　and　three　aggregation　functions,　eighteen　WQI　models　were　developed.　To　reduce　the　influence　of　spatiotemporal　heterogeneity,　the　assessment　models　for　the　different　water　quality　characteristics　were　proposed　respectively.　To　validate　the　performance　of　improved　model,　the　monthly　water　quality　monitoring　data　of　16　sampling　sites　in　Chaohu　Lake　during　2016-2020　was　used.　Among　them,　totally　10　water　quality　indicators　were　selected,　including　TN,　TP,　etc.　The　results　showed　high　accuracy　and　reliability　of　the　improved　WQI　assessment　models.　The　model　improved　by　CatBoost　and　EWM　had　low　uncertainty　(0.559-0.903)　than　SVM　and　LR　(0.576-1.034).　The　sensitivity　of　the　models　improved　by　six　combined　weights　is　ranked　as　W-AE　＞　W-AC　＞　W-AS　＞　W-AX　＞　W-ALGB　＞　W-AL.　The　uncertainty　of　the　models　improved　by　the　three　aggregation　functions　were　ranked　as　SGM　＞　SWM　＞　WQM　and　the　sensitivity　were　ranked　as　WQM　＞　SWM　＞　SGM.　Compared　with　WQM　and　SWM,　SGM　could　reflect　the　water　quality　spatiotemporal　heterogeneity　more　accurately.　The　WQM(AE),　SGM(AC)　and　SWMAC　models　were　recommended　for　assessing　water　bodies　with　good　quality,　poor　quality　and　heterogeneity　respectively.　Chaohu　Lake　was　mainly　Class　II　and　Class　III　water.　East　had　better　water　quality　than　the　west.　Water　quality　in　summer　and　autumn　was　better　than　in　spring　and　winter.　This　study　can　provide　theoretical　support　for　related　water　quality　assessment　work.