Based　on　the　idea　of　secondary　decomposition　and　ensemble　learning,　we　build　the　VMD-EEMD-DE-ELM-DE-ELM　model,　select　soybeans,　wheat　and　rice　futures　listed　on　the　CBOT　exchange　as　representatives　of　international　grain　futures,　and　predict　its　future　price　trend.　In　view　of　the　existing　research　that　directly　ignore　the　residual　items　after　VMD　decomposition,　we　introduce　the　idea　of　secondary　decomposition　to　perform　the　EEMD　decomposition　and　ensemble　prediction　of　its　residual　items　for　the　first　time.　This　method　can　capture　the　rich　information　contained　in　the　residual　items,　thereby　helping　to　improve　the　model＇s　prediction　effect　on　the　original　sequence.　At　the　same　time,　because　of　the　shortcomings　of　the　existing　model　which　use　equal　weights　to　reconstruct　the　prediction　results　of　components,　we　draw　on　the　idea　of　ensemble　learning　and　introduces　the　DE-ELM　meta-learner　to　optimize　the　reconstruction　weights　to　obtain　the　best　overall　prediction　results　of　the　model.　The　empirical　results　show　that　the　model　proposed　by　us　has　a　significant　predictive　advantage　over　the　existing　models.　©　2021,　Editorial　Board　of　Journal　of　Systems　Engineering　Society　of　China.　All　right　reserved.