A　healthy　ecological　environment　forms　a　crucial　foundation　for　the　sustainable　development　of　both　nations　and　humanity.　In　the　domain　of　ecological　environment　assessment,　the　comprehensive　indicator　system　model　represents　the　mainstream　evaluation　approach,　both　domestically　and　internationally.　The　extensive　application　of　big　geodata　within　this　context　offers　significant　potential　for　addressing　ecological　problems　characterized　by　vast　scales,　intricate　processes,　and　a　variety　of　influencing　factors.　However,　as　the　acquisition　of　big　geodata　becomes　increasingly　accessible,　the　coverage　of　the　index　system　has　significantly　expanded,　raising　the　pivotal　issue　of　objectively　and　scientifically　selecting　crucial　indicators　capable　of　representing　the　distinctive　characteristics　of　the　study　area.　This　challenge　is　particularly　critical　in　today＇s　ecological　health　assessment.　The　Pressure-State-Response　(PSR)　model　offers　a　causal　perspective,　comprehensively　considering　the　systemic　relationships　between　the　ecological　environment　and　human　socioeconomic　activities.　The　Ecological　Hierarchy　Network　(EHN)　model　is　capable　of　reflecting　the　overlap　and　interconnections　between　upper　and　lower-layer　indicators.　In　this　study,　by　integrating　the　frameworks　of　PSR　and　EHN　and　taking　into　account　the　potential　information　overlap　from　multiple　available　parameters,　we　established　a　five-layer　networked　indicator　system　consisting　of　the　Target　Layer,　Criteria　Layer,　Element　Layer,　Indicator　Layer,　and　Homogeneous　Indicator　Layer.　We　also　proposed　a　two-stage　adaptive　indicator　reduction　model　that　combines　Homogeneous　Indicator　Layer　reduction　using　the　Technique　for　Order　Preference　by　Similarity　to　Ideal　Solution　(TOPSIS)　and　Indicator　Layer　reduction　based　on　target　optimization　theory.　Combining　both　approaches,　we　developed　an　adaptive　indicator　reduction　model　tailored　for　ecological　environmental　health　assessment.　Leveraging　big　geodata　comprising　remote　sensing　thematic　products,　topography,　meteorology,　soil,　and　population　information,　we　applied　the　proposed　model　to　assess　the　ecological　health　of　seven　ecologically　diverse　regions　in　China,　including　Yunnan,　Fujian,　Beijing-Tianjin-Hebei,　Shaanxi,　Hubei,　Xinjiang,　and　Jilin　during　the　period　2001-2021.　The　results　show　that:　(1)　The　selected　indicators　obtained　through　the　two-stage　indicator　adaptive　reduction　model　effectively　reflected　the　distinct　characteristics　of　ecosystems　in　different　regions.　Furthermore,　indicators　with　higher　weights　among　the　selected　ones　have　been　widely　employed　in　constructing　indicator　systems　across　various　regions.　These　findings　highlighted　the　universality　and　rationality　of　both　the　constructed　indicator　system　and　the　two-stage　indicator　adaptive　reduction　model,　effectively　mitigating　the　subjectivity　associated　with　manual　indicator　system　construction;　(2)　The　spatial　distribution　and　temporal　trends　of　the　ecological　environment　health　of　the　seven　regions　aligned　with　real-world　conditions　and　were　corroborated　by　existing　literature　and　data,　which　indicated　the　effectiveness　of　the　model　proposed　in　this　study.　The　proposed　models　presented　in　this　paper　can　serve　as　a　reference　for　constructing　indicator　systems　and　selecting　indicators　in　other　domains　and　provide　methodological　support　for　ecological　environment　health　assessment　across　diverse　regions　on　a　large　scale.　©　2024　Science　Press.　All　rights　reserved.