This　paper　proposes　a　new　synthetic　measure　of　node　centrality,　namely,　multi　-information　fused　degree　of　grey　incidence　centrality　(MIFDC),　which　is　used　to　evaluate　the　importance　of　nodes　and　identify　influential　nodes　in　complex　networks.　It　is　the　first　time　that　the　grey　incidence　analysis　(GIA)　and　the　D　-S　evidence　theory　are　combined　to　identify　influential　nodes　in　complex　networks　in　the　MIFDC　method.　The　proposed　MIFDC　measure　comprehensively　considers　the　information　of　multiple　centrality　measures　and　can　correct　the　subjective　bias　problem　in　the　selection　process　of　the　grey　incidence　operator.　To　verb　the　performance　of　the　proposed　method　the　MIFDC　method　is　applied　to　identify　influential　nodes　in　two　real　networks,　the　Advanced　Research　Project　Agency　(ARPA)　network,　and　the　terrorist　relationship　network　The　application　results　show　that　the　MIFDC　method　can　effectively　identify　the　influential　nodes　of　the　network.