There　exists　a　complex　interplay　between　the　aggregate　risk　of　the　stock　market　and　the　risks　inherent　to　various　industries.　However,　existing　studies　have　largely　overlooked　the　disparities　in　risk　contagion　effects　between　different　industries　and　the　overall　market　across　diverse　time　scales,　and　there　remains　instability　in　delineating　risk　frequency　domains.　Consequently,　it　becomes　challenging　to　fully　unravel　the　intricate　correlation　dynamics　between　different　industries　and　the　broader　stock　market　across　varying　temporal　dimensions.　Therefore,　this　paper　takes　the　China　stock　market　as　a　representative　of　emerging　markets,　selects　eleven　distinct　industry　indices　along　with　the　Shanghai-Shenzhen　300　Index　(CSI　300　Index),　and　introduces　the　Variable　Mode　Decomposition　(VMD)　algorithm　to　extract　intricate　information　from　time　series　data.　Additionally,　the　Fuzzy　Entropy　(FE)　algorithm　is　employed　to　effectively　reconstruct　different　frequency　domains.　Furthermore,　this　paper　integrates　the　strengths　of　the　Generalized　Autoregressive　Conditional　Heteroskedasticity　(GARCH)　model,　the　Copula　function,　and　the　Conditional　Value　at　Risk　(CoVaR)　model.　By　constructing　the　novel　VMD-FE-GARCH-Copula-CoVaR　hybrid　model,　this　research　aims　to　explore　the　risk　contagion　characteristics　of　various　industries　and　the　Shanghai-Shenzhen　300　Index　across　different　time　scales,　offering　a　fresh　perspective　for　the　paper　of　risk　contagion　within　stock　market　industries.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2025.