Named　Entity　Recognition　(NER)　is　an　important　basic　task　in　natural　language　processing　(NLP).　In　recent　years,　the　method　of　word　representations　enhancement　by　character　embedding　has　significantly　enhanced　the　effect　of　entity　recognition.　However,　this　kind　of　character　embedding　method　only　works　on　alphabetic　spelling　words　such　as　English,　and　the　same　method　is　not　suitable　for　Chinese.　Aiming　at　the　inherent　characteristics　of　Chinese　as　morpheme　writing,　we　propose　a　novel　neural　network　model　based　on　CNN-BiLSTM-CRF　in　this　paper.　Convolution　neural　network　(CNN)　extracts　the　glyph　embeddings　with　morphological　features　from　each　Chinese　character,　which　are　concatenated　with　the　character　embeddings　with　semantic　feature　information　and　fed　to　the　BiLSTM-CRF　network.　We　evaluate　our　model　on　the　third　SIGHAN　Bakeoff　MSRA　dataset　for　simplified　Chinese　NER　task.　The　experimental　results　show　that　our　model　reaches　91.09%　in　F-scores　which　does　not　rely　on　the　hand-designed　features　and　domain　knowledge.　©　2018　IEEE.