The　prediction　research　of　the　stock　market　prices　is　of　great　significance.　Based　on　the　secondary　decomposition　techniques　of　variational　mode　decomposition　(VMD)　and　ensemble　empirical　mode　decomposition　(EEMD),　this　paper　constructs　a　new　hybrid　prediction　model　by　combining　with　extreme　learning　machine　(ELM)　optimized　by　the　differential　evolution　(DE)　algorithm.　The　hybrid　model　applies　VMD　technology　to　the　original　stock　index　price　sequence　to　obtain　different　modal　components　and　the　residual　item,　then　applies　EEMD　technology　to　the　residual　item,　and　then　superimposes　the　prediction　results　of　the　DE-ELM　model　for　each　modal　component　and　the　residual　item　to　obtain　the　final　prediction　results.　In　order　to　verify　the　validity　of　the　model,　this　paper　constructs　a　series　of　benchmark　models　and,　respectively,　tests　the　samples　of　the　S&P　500　index　and　the　HS300　index　by　one-step,　three-step,　and　five-step　forward　forecasting.　The　empirical　results　show　that　the　hybrid　model　proposed　in　this　paper　achieves　the　best　prediction　performance　in　all　prediction　scenarios,　which　indicates　that　the　modeling　idea　focusing　on　the　residual　term　effectively　improves　the　prediction　performance　of　the　model.　In　addition,　the　prediction　effect　of　the　model　combined　with　the　decomposition　technology　is　superior　to　the　single　DE-ELM　model,　where　the　secondary　decomposition　technique　has　a　significant　decomposition　advantage　compared　to　the　single　decomposition　technique.　©　2020　Zhenpeng　Tang　et　al.