The task of named entity recognition is to locate the entities in the text and classify them into predefined categories. The current mainstream Chinese named entity recognition models are characterbased named entity recognition models which word segmentation is required before using syntactic features， syntactic information of sentences cannot be well utilized as a result. In addition， the characterbased models cannot make use of the prior dictionary information and the pictographic information contained in Chinese radicals. To solve the above problems， this paper proposes a multifeature Chinese named entity recognition model combining syntax and multigranularity semantic information. The experiments demonstrate that the proposed model is better than the current mainstream Chinese named entity recognition models， the influence of various features on the Chinese entity recognition effect is analyzed through experiments as well.