Forecasting　the　price　of　crude　oil　is　a　challenging　task.　To　improve　this　forecasting,　this　paper　proposes　a　novel　hybrid　method　that　uses　an　integrated　data　fluctuation　network　(DFN)　and　several　artificial　intelligence　(AI)　algorithms,　named　DIN-AI　model.　In　the　proposed　DFN-AI　model,　a　complex　network　time　series　analysis　technique　is　performed　as　a　preprocessor　for　the　original　data　to　extract　the　fluctuation　features　and　reconstruct　the　original　data,　and　then　an　artificial　intelligence　tool,　e.g.,　BPNN,　RBFNN　or　ELM,　is　employed　to　model　the　reconstructed　data　and　predict　the　future　data.　To　verify　these　results　we　examine　the　daily,　weekly,　and　monthly　price　data　from　the　crude　oil　trading　hub　in　Cushing,　Oklahoma.　Empirical　results　demonstrate　that　the　proposed　DIN-AI　models　(i.e.,　DFN-BP,　DEN-RBF,　and　DFN-ELM)　perform　significantly　better　than　their　corresponding　single　AI　models　in　both　the　direction　and　level　of　prediction.　This　confirms　the　effectiveness　of　our　proposed　modeling　of　the　nonlinear　patterns　hidden　in　crude　oil　prices.　In　addition,　our　proposed　DEN-AI　methods　are　robust　and　reliable　and　are　unaffected　by　random　sample　selection,　sample　frequency,　or　breaks　in　sample　structure.