Faced　with　the　growing　research　toward　crude　oil　price　fluctuations　influential　factors　following　the　accelerated　development　of　Internet　technology,　accessible　data　such　as　Google　search　volume　index　(GSVI)　are　increasingly　quantified　and　incorporated　into　forecasting　approaches.　In　this　study,　we　apply　multi-scale　data　that　including　both　traditional　economic　data　and　GSVI　data　reflecting　macro　and　micro　mechanisms　affecting　crude　oil　price　respectively,　so　as　to　reduce　the　forecasting　deviation　and　improve　the　forecasting　accuracy　at　source.　In　addition,　a　new　hybrid　approach:　K-meansKPCAKELM　based　on　＇divide　and　conquer＇　strategy　is　proposed　for　deeply　exploring　the　information　of　above　multi-data　so　that　improve　monthly　crude　oil　price　forecasting　accuracy.　Empirical　results　can　be　analyzed　from　data　and　method　levels.　At　the　data　level,　GSVI　data　perform　better　than　economic　data　in　level　forecasting　accuracy　but　with　opposite　performance　in　directional　forecasting　accuracy　because　of　＇Herd　Behavior＇,　while　hybrid　data　combined　their　advantages　and　obtain　best　forecasting　performance　in　both　level　and　directional　accuracy.　At　the　method　level,　the　approaches　with　＇divide　and　conquer＇　strategy　gain　a　better　forecasting　performance,　which　demonstrates　that　＇divide　and　conquer＇　strategy　can　effectively　improve　the　forecasting　performance.　©　2021　Elsevier　Ltd