Integrating　lexical　knowledge　in　Chinese　named　entity　recognition　(NER)　has　been　proven　effective.　Among　the　existing　methods,　Flat-LAttice　Transformer　(FLAT)　has　achieved　great　success　in　both　performance　and　efficiency.　FLAT　performs　lexical　enhancement　for　each　sentence　by　constructing　a　flat　lattice　(i.e.,　a　sequence　of　tokens　including　the　characters　in　a　sentence　and　the　matched　words　in　a　lexicon)　and　calculating　self-attention　with　a　fully-connected　structure.　However,　the　different　interactions　between　tokens,　which　can　bring　different　aspects　of　semantic　information　for　Chinese　NER,　cannot　be　well　captured　by　self-attention　with　a　fully-connected　structure.　In　this　paper,　we　propose　a　novel　Multi-View　Transformer　(MVT)　to　effectively　capture　the　different　interactions　between　tokens.　We　first　define　four　views　to　capture　four　different　token　interaction　structures.　We　then　construct　a　view-aware　visible　matrix　for　each　view　according　to　the　corresponding　structure　and　introduce　a　view-aware　dot-product　attention　for　each　view　to　limit　the　attention　scope　by　incorporating　the　corresponding　visible　matrix.　Finally,　we　design　three　different　MVT　variants　to　fuse　the　multi-view　features　at　different　levels　of　the　Transformer　architecture.　Experimental　results　conducted　on　four　public　Chinese　NER　datasets　show　the　effectiveness　of　the　proposed　method.　Specifically,　on　the　most　challenging　dataset　Weibo,　which　is　in　an　informal　text　style,　MVT　outperforms　FLAT　in　F1　score　by　2.56%,　and　when　combined　with　BERT,　MVT　outperforms　FLAT　in　F1　score　by　3.03%.