Indoor　thermal　environment　design　parameters　have　a　significant　impact　on　human　thermal　comfort,　health　and　building　energy　consumption.　The　methods　of　determining　indoor　thermal　environment　design　parameters　in　China　and　around　the　world　are　mainly　based　on　the　PMV　model.　This　method　often　results　in　certain　errors　deviations　from　the　actual　situation　and　has　difficulty　reflecting　the　differences　in　the　thermal　environmental　requirements　in　different　climate　regions,　seasons,　building　types,　etc.　This　paper　proposes　a　data-driven　method　based　on　Chinese　Thermal　Comfort　Database　and　uses　a　logistic　regression　to　obtain　the　indoor　air　temperature　range　that　80%　of　occupants　find　thermally　acceptable　under　different　conditions.　The　results　indicate　that　the　prediction　of　this　model　are　in　good　agreement　with　the　actual　data　and　have　as　higher　accuracy　in　predicting　the　percentage　of　thermal　acceptability　than　a　linear　regression　method.　This　model　shows　that　different　regions,　seasons,　and　building　types　have　different　acceptable　temperature　ranges　and　that　the　acceptable　air　temperature　ranges　are　on　average　2.0　°C　wider　than　the　ISO　7730　and　0.7　°C　wider　than　the　GB50736-2012.　This　method　is　conducive　to　the　determination　of　air　temperature　thresholds　and　better　reflects　people＇s　equirements　for　thermal　environment　under　different　conditions.　©　2021　Elsevier　B.V.