To　optimize　the　water　quality　index　(WQI)　assessment　model,　this　study　upgraded　the　parameter　weight　values　and　aggregation　functions.　We　determined　the　combined　weights　based　on　machine　learning　and　game　theory　to　improve　the　accuracy　of　the　models,　and　proposed　new　aggregation　functions　to　reduce　the　uncertainty　of　the　model.　A　new　water　quality　assessment　system　was　established,　and　took　the　Chaobai　River　Basin　as　a　case　study.　To　optimize　the　weight,　two　combined　weights　were　established　based　on　game　theory.　The　weight　CWAE　was　combined　by　the　Analytic　Hierarchy　Process　(AHP)　and　Entropy　Weight　Method　(EWM).　The　weight　CWAL　was　combined　by　AHP　and　machine　learning　(LightGBM).　CWAL　was　judged　to　be　an　optimal　composite　weight　by　comparing　the　coefficient　of　variation　(CV)　values　and　the　Kaiser-Meyer-Olkin　(KMO)　extracted　values.　To　reduce　the　uncertainty　of　the　model,　we　proposed　two　aggregation　functions,　the　Sinusoidal　Weighted　Mean　(SWM)　and　the　Log-weighted　Quadratic　Mean　(LQM).　The　three　water　quality　assessment　models　(WQIS,　WQIL　and　WQIW)　were　established　based　on　the　optimal　weights　besides.　All　three　models　had　good　reliability.　Both　WQIS　and　WQIW　models　had　low　eclipsing　problems　(25.49%　and　18.63%).　The　accuracy　of　the　models　was　ranked　as　WQIS　＞　WQIW　＞　WQIL.　The　uncertainty　of　WQIs　(0.000)　in　assessing　poor　water　quality　was　low,　and　so　was　WQIW　(0.259)　in　assessing　good　water　quality.　Overall,　the　WQIS　model　was　recommended　for　assessing　poor　water　quality　and　the　WQIW　model　was　recommended　for　assessing　good　water　quality.　The　assessment　results　of　WQIS　showed　that　the　Chaobai　River　Basin　was　＂slightly　polluted＂,　and　the　water　quality　upstream　was　better　than　that　downstream.　TN　was　the　main　pollutant　in　the　basin,　and　there　was　slight　pollution　with　CODMn,　CODCr,　BOD5,　etc.　There　was　little　metal　contamination,　only　a　few　months　exceeded　Class　I.　The　model　established　in　this　study　can　provide　a　reference　for　the　same　type　work　of　water　quality　assessment.　The　assessment　results　can　provide　a　scientific　basis　for　the　protection　of　the　regional　water　environment.