Identifying　influential　nodes　in　complex　networks　has　attracted　the　attention　of　many　researchers　in　recent　years.　However,　due　to　the　high　time　complexity,　methods　based　on　global　attributes　have　become　unsuitable　for　large-scale　complex　networks.　In　addition,　compared　with　methods　considering　only　a　single　attribute,　considering　multiple　attributes　can　enhance　the　performance　of　the　method　used.　Therefore,　this　paper　proposes　a　new　multiple　local　attributes-weighted　centrality　(LWC)　based　on　information　entropy,　combining　degree　and　clustering　coefficient;　both　one-step　and　two-step　neighborhood　information　are　considered　for　evaluating　the　influence　of　nodes　and　identifying　influential　nodes　in　complex　networks.　Firstly,　the　influence　of　a　node　in　a　complex　network　is　divided　into　direct　influence　and　indirect　influence.　The　degree　and　clustering　coefficient　are　selected　as　direct　influence　measures.　Secondly,　based　on　the　two　direct　influence　measures,　we　define　two　indirect　influence　measures:　two-hop　degree　and　two-hop　clustering　coefficient.　Then,　the　information　entropy　is　used　to　weight　the　above　four　influence　measures,　and　the　LWC　of　each　node　is　obtained　by　calculating　the　weighted　sum　of　these　measures.　Finally,　all　the　nodes　are　ranked　based　on　the　value　of　the　LWC,　and　the　influential　nodes　can　be　identified.　The　proposed　LWC　method　is　applied　to　identify　influential　nodes　in　four　real-world　networks　and　is　compared　with　five　well-known　methods.　The　experimental　results　demonstrate　the　good　performance　of　the　proposed　method　on　discrimination　capability　and　accuracy.