Title: Tree Structure LSTM for Chinese Named Entity Recognition. Abstract: In this paper, we remodel the bi-directional LSTM (Bi-LSTM) network for Chinese Named Entity Recognition (NER). We convert LSTM from chain-like structure into tree structure which is fixed to the dependency parsing tree of the sentence. The new structure model can fully leverage the syntax information of the sentence and has the capability of capturing long-range dependencies. In addition, we use dependency parsing label embedding to improve the performance of our approach. Experimental studies on four benchmarking Chinese NER datasets have verified the effectiveness of our approach.