Research　on　forecasting　electricity　prices　is　of　great　significance　to　market　participants.　It　is　very　difficult,　however,　to　forecast　the　electricity　price　series　because　of　its　nonlinearity　and　high　volatility.　Considering　that　the　existing　studies　directly　ignore　the　important　information　contained　in　the　residual　term　(Res.)　after　variational　modal　decomposition　(VMD),　this　paper　introduces　a　two-layer　decomposition　technique　based　on　the　combination　of　VMD　technology　and　ensemble　empirical　modal　decomposition　(EEMD),　carrying　out　EEMD　decomposition　on　the　residual　term　after　VMD　decomposition　to　improve　the　overall　prediction　accuracy　of　the　model.　At　the　same　time,　in　order　to　address　the　defects　of　the　existing　hybrid　model　prediction　methods-which　use　equal　weights　to　reconstruct　the　prediction　results-this　paper　draws　on　the　idea　of　ensemble　learning,　combining　the　extreme　learning　machine　(ELM)　optimized　by　the　differential　evolution　(DE)　algorithm,　introducing　the　DE-ELM　meta-learner　to　optimize　the　reconstruction　weights　of　the　prediction　components,　and　constructing　a　new　hybrid　model　VMD-Res.-EEMD-DE-ELM-DE-ELM　to　obtain　better　prediction　results.　In　order　to　verify　the　model＇s　prediction　performance,　this　paper　uses　electricity　prices　from　Australian　and　Spanish　electricity　markets　for　empirical　analysis.　The　results　show　that　the　hybrid　model　proposed　in　this　paper　has　significant　forecasting　advantages.