In　the　continuous-casting　process,　mold-level　control　is　one　of　the　most　important　factors　that　ensures　the　quality　of　high-efficiency　continuous　casting　slabs.　In　traditional　mold-level　prediction　control,　the　mold-level　prediction　accuracy　is　low,　and　the　calculation　cost　is　high.　In　order　to　improve　the　prediction　accuracy　for　mold-level　prediction,　an　adaptive　hybrid　prediction　algorithm　is　proposed.　This　new　algorithm　is　the　combination　of　empirical　mode　decomposition　(EMD),　variational　mode　decomposition　(VMD),　and　support　vector　regression　(SVR),　and　it　effectively　overcomes　the　impact　of　noise　on　the　original　signal.　Firstly,　the　intrinsic　mode　functions　(IMFs)　of　the　mold-level　signal　are　obtained　by　the　adaptive　EMD,　and　the　key　parameter　of　the　VMD　is　obtained　by　the　correlation　analysis　between　the　IMFs.　VMD　is　performed　based　on　the　key　parameter　to　obtain　several　IMFs,　and　the　noise　IMFs　are　denoised　by　wavelet　threshold　denoising　(WTD).　Then,　SVR　is　used　to　predict　each　denoised　component　to　obtain　the　predicted　IMF.　Finally,　the　predicted　mold-level　signal　is　reconstructed　by　the　predicted　IMFs.　In　addition,　compared　with　WTD-SVR　and　EMD-SVR,　VMD-SVR　has　a　competitive　advantage　against　the　above　three　methods　in　terms　of　robustness.　This　new　method　provides　a　new　idea　for　mold-level　prediction.