An　important　feature　of　the　outbreak　of　systemic　financial　risk　is　that　the　linkage　and　contagion　of　risk　amongst　the　various　sub-markets　of　the　financial　system　have　increased　significantly.　In　addition,　research　on　the　prediction　of　systemic　financial　risk　plays　a　significant　role　in　the　sustainable　development　of　the　financial　market.　Therefore,　this　paper　takes　China＇s　financial　market　as　its　research　object,　considers　the　risks　co-activity　among　major　financial　sub-markets,　and　constructs　a　financial　composite　indicator　of　systemic　stress　(CISS)　for　China,　describing　its　financial　systemic　stress　based　on　12　basic　indicators　selected　from　the　money　market,　bond　market,　stock　market,　and　foreign　exchange　market.　Furthermore,　drawing　on　the　decomposition　and　integration　technology　in　the　TEI@I　complex　system　research　methodology,　this　paper　introduces　advanced　variational　mode　decomposition　(VMD)　technology　and　extreme　learning　machine　(ELM)　algorithms,　constructing　the　VMD-DE-ELM　hybrid　model　to　predict　the　systemic　risk　of　China＇s　financial　market.　According　to　eRMSE,　eMAE,　and　eMAPE,　the　prediction　model＇s　multistep-ahead　forecasting　effect　is　evaluated.　The　empirical　results　show　that　the　China＇s　financial　CISS　constructed　in　this　paper　can　effectively　identify　all　kinds　of　risk　events　in　the　sample　range.　The　results　of　a　robustness　test　show　that　the　overall　trend　of　China＇s　financial　CISS　and　its　ability　to　identify　risk　events　are　not　affected　by　parameter　selection　and　have　good　robustness.　In　addition,　compared　with　the　benchmark　model,　the　VMD-DE-ELM　hybrid　model　constructed　in　this　paper　shows　superior　predictive　ability　for　systemic　financial　risk.