The　prediction　of　crude　oil　prices　has　important　research　significance.　The　paper　contributes　to　the　literature　of　hybrid　models　for　forecasting　crude　oil　prices.　We　apply　ensemble　empirical　mode　decomposition　(EEMD)　to　decompose　the　residual　term　(RES),　which　contains　complex　information　after　variational　mode　decomposition　(VMD),　further　combining　with　a　kernel　extreme　learning　machine　(KELM)　optimized　by　particle　swarm　optimization　(PSO)　to　construct　the　VMD-RES.-EEMD-PSO-KELM　model.　In　order　to　verify　the　validity　of　the　model,　this　paper　conducts　empirical　analyses　of　Brent　crude　oil　and　West　Texas　Intermediate　(WTI)　crude　oil.　The　empirical　results　show　that　the　prediction　model　proposed　in　this　paper　improves　the　prediction　accuracy　of　crude　oil　prices.　©　2021　Elsevier　Ltd