Understanding　the　spatiotemporal　dynamics　and　key　drivers　of　recycling　enterprises　is　essential　for　optimizing　resource　recovery　systems　and　advancing　sustainable　development　in　China.　This　study　adopts　a　multisource　big　data　approach,　integrating　geospatial,　economic,　and　environmental　datasets　from　300　cities　between　1987　and　2024,　and　applies　spatial　analysis　and　Random　Forest　models　to　examine　5,171　registered　recycling　enterprises.　Results　reveal　strong　spatial　concentration　in　eastern　coastal　provinces　like　Jiangsu　(over　500　enterprises),　Shandong,　and　Zhejiang.　Despite　gradual　westward　expansion　since　2010,　the　western　region　accounts　for　only　18.4%　of　the　total.　The　industrial　chain　presents　spatial　heterogeneity:　upstream　and　downstream　enterprises　are　dispersed,　while　65.5%　of　midstream　enterprises　cluster　in　the　Yangtze　River　Delta.　Random　Forest　analysis　shows　that　patent　grants　(0.327)　and　retail　sales　(0.273)　are　the　top　national　predictors.　Regionally,　innovation　dominates　in　eastern　(0.311)　and　central　(0.500)　China,　with　carbon　emissions　also　influential　(0.124　and　0.149),　whereas　market　size　leads　in　the　west　(0.497).　Temporally,　enterprise　growth　evolved　from　market-　and　labororiented　drivers　(2002-2009),　to　innovation-driven　expansion　(2010-2020),　and　finally　to　an　environmental　governance　phase　after　2021,　where　carbon　emissions　(0.219)　became　the　primary　spatial　determinant.　By　leveraging　big　data　and　machine　learning,　this　study　provides　insights　for　optimizing　recycling　networks　and　enhancing　regional　sustainability.