Named　Entity　Recognition　(NER)　is　a　crucial　step　in　natural　language　processing　(NLP).　Recently,　some　researches　work　on　enhancing　the　word　representations　by　character-level　extensions　in　English　and　have　achieve　excellent　performance.　The　same　method　applies　to　Chinese　as　well.　In　this　paper,　we　introduce　this　idea　into　character-based　Chinese　NER.　The　method　uses　the　information　from　character-level　and　glyph-level　representations　at　the　same　time　to　achieve　high　accuracy.　In　addition,　we　propose　an　attentional　model　which　is　able　to　decide　how　much　information　to　use　from　a　character-level　or　glyph-level　component　dynamically.　We　validate　the　proposed　method　on　the　third　SIGHAN　Bakeoff　MSRA　datasets.　Experimental　results　show　that　the　proposed　approaches　are　effective　in　improving　the　performance　of　Chinese　NER　and　achieve　91.11%　F1　without　any　additional　handmade　features.　©　2019　Association　for　Computing　Machinery.