The　prediction　of　mold　level　is　a　basic　and　key　problem　of　continuous　casting　production　control.　Many　current　techniques　fail　to　predict　the　mold　level　because　of　mold　level　is　non-linear,　non-stationary　and　does　not　have　a　normal　distribution.　A　hybrid　model,　based　on　empirical　mode　decomposition　(EMD)　and　support　vector　regression　(SVR),　is　proposed　to　solve　the　mold　level　in　this　paper.　Firstly,　the　EMD　algorithm,　with　adaptive　decomposition,　is　used　to　decompose　the　original　mold　level　signal　to　many　intrinsic　mode　functions　(IMFs).　Then,　the　SVR　model　optimized　by　genetic　algorithm　(GA)　is　used　to　predict　the　IMFs　and　residual　sequences.　Finally,　the　equalization　of　the　predict　results　is　reconstructed　to　obtain　the　predict　result.　Several　hybrid　predicting　methods　such　as　EMD　and　autoregressive　moving　average　model　(ARMA),　EMD　and　SVR,　wavelet　transform　(WT)　and　ARMA,　WT　and　SVR　are　discussed　and　compared　in　this　paper.　These　methods　are　applied　to　mold　level　prediction,　the　experimental　results　show　that　the　proposed　hybrid　method　based　on　EMD　and　SVR　is　a　powerful　tool　for　solving　complex　time　series　prediction.　In　view　of　the　excellent　generalization　ability　of　the　EMD,　it　is　believed　that　the　hybrid　algorithm　of　EMD　and　SVR　is　the　best　model　for　mold　level　predict　among　the　six　methods,　providing　a　new　idea　for　guiding　continuous　casting　process　improvement.