Accurate　evaluation　of　groundwater　quality　and　identification　of　key　characteristics　are　essential　for　maintaining　groundwater　resources.　The　purpose　of　this　study　is　to　strengthen　water　quality　evaluation　through　the　SHAP　and　XGBoost　algorithms,　analyze　the　key　indicators　affecting　water　quality　in　depth,　and　quantify　their　impact　on　groundwater　quality　through　interpretable　tools.　The　XGBoost　algorithm　shows　that　zinc　(0.183),　nitrate　(0.159),　and　chloride　(0.136)　are　the　three　indicators　with　the　highest　weight.　The　SHAP　algorithm　shows　that　zinc　(34.62%),　nitrate　(17.65%),　and　chloride　(16.98%)　have　higher　contribution　values,　which　explains　the　output　results　of　XGBoost.　According　to　the　calculation　scores　and　classification　standards　of　the　water　quality　model,　49%　of　the　groundwater　samples　in　the　study　area　have　excellent　water　quality,　33%　of　the　samples　are　better,　and　18%　of　the　samples　are　polluted.　The　results　of　positive　matrix　factorization　(PMF)　show　that　natural　conditions,　metal　processing,　metal　smelting　and　mining,　and　agricultural　activities　all　cause　pollution　to　groundwater.　Zinc,　chloride,　nitrate,　and　manganese　were　the　key　variables　determined　by　the　SHAP　algorithm　to　explain　the　vast　majority　of　human　health　risk　sources.　These　findings　indicate　that　interpretable　machine　learning　not　only　improves　the　correlation　of　water　quality　assessment　but　also　quantifies　the　judgment　basis　of　each　sample　and　helps　to　track　key　pollution　indicators.