The　increasing　free　access　of　the　Internet　provides　us　with　favorable　circumstances　to　investigate　search　engine　index　reflecting　more　and　more　personal　behavior　information.　Part　of　valuable　travel　search　information　can　assist　us　to　achieve　more　robust　and　reliable　prediction　of　metro　passenger　flow.　Inspired　by　this,　the　paper　develops　a　new　multi-source　time　series　fusion　and　direct　interval　prediction　approach　to　grasp　the　dynamic　law　of　metro　passenger　flow　effectively.　Multi-source　index　regarding　metro　travel　from　three　major　search　engines　(Baidu,　Sogou　and　360)　in　China　are　screened　out　and　fused　into　the　powerful　predictors.　By　integrating　an　optimized　multivariate　mode　decomposition　strategy　and　long　short-term　memory　model,　lower　and　upper　bounds　of　prediction　interval　are　estimated　directly　by　a　multi-objective　framework　that　combines　the　advantages　of　both　the　deep　learning　models　long　short-term　memory　and　the　ensemble　learning　approach.　Especially,　two　sets　of　real　experiment　data　of　Beijing　and　Shanghai　metro　systems　are　employed　to　test　our　approach.　Findings　show　that　fusion　of　multi-source　index　information　promotes　the　predictability　of　metro　passenger　flow,　contributing　to　improving　operation　management　and　service　quality.　(C)　2022　Elsevier　B.V.　All　rights　reserved.