The　smart　substations　is　the　core　part　of　smart　grids　which　affect　the　development　of　the　economy　and　people＇s　livelihood.　This　paper　constructs　a　comprehensive　benefit　evaluation　index　system　of　smart　substation　construction　project　from　four　dimensions:　intelligent　benefit,　economic　benefit,　green　benefit,　and　safe　benefit　to　meet　the　requirements　of　national　development.　The　attribute　value　of　the　evaluation　index　is　hybrid　information　including　language　variable,　exact　number,　and　normal　distribution　interval　number,　which　can　handle　the　complex　sources　of　information.　On　this　basis,　this　study　constructs　a　hybrid　multi-criteria　decision-making　model　for　hybrid　information　based　on　intuitionistic　fuzzy　theory,　subjective　and　objective　weighting　method,　and　improved　TOPSIS　evaluation　method.　Firstly,　the　model　uses　different　transformation　rules　to　unify　the　hybrid　information　into　the　form　of　intuitionistic　fuzzy　numbers　based　on　intuitionistic　fuzzy　theory.　Secondly,　based　on　the　cooperative　game　model,　this　paper　uses　a　weighting　method　which　combines　subjective　weighting　method　(group　eigenvalue　method)　and　objective　weighting　method　(intuitionistic　fuzzy　entropy)　to　determine　the　comprehensive　weight　of　indicators,　which　can　avoid　the　limitations　of　using　single　weighting　method　and　makes　the　weights　more　scientific　and　reasonable.　Thirdly,　an　improved　TOPSIS　evaluation　model　based　on　intuitionistic　fuzzy　sets　is　proposed.　The　Euclidean　distance　in　traditional　TOPSIS　method　is　replaced　by　cosine　similarity,　which　overcomes　the　shortcoming　of　using　Euclidean　distance.　Finally,　this　paper　analyzes　10　cases　of　smart　substation　construction　projects　and　puts　forward　improvement　strategies　based　on　the　results,　the　results　prove　that　the　model　is　feasible　and　it　can　assist　decision　makers　in　making　scientific　decisions　based　on　different　situations.　In　addition,　this　hybrid　multi-criteria　decision-making　model　is　compared　with　three　other　models,　which　proves　that　the　proposed　model　has　good　robustness　and　sensitivity.