Abstract: In the task of Chinese named entity recognition, although the fusion of word information and vocabulary information can enrich text features, a word may correspond to multiple candidate words such that the vocabulary conflict is easily caused. Moreover, the fusion of irrelevant vocabulary information will affect the recognition effect of the model. Aiming the above shortcoming, this paper proposes a Chinese named entity recognition method based on hierarchical adjustment of dictionary information. Firstly, all potential words are layered according to the length of words, and the weight of low-level words is adjusted through high-level word feedback to retain more useful information, so as to alleviate the problem of semantic deviation and reduce the impact of word conflict. Then, the word information is spliced into the word information to enhance the representation of text feature. It is shown via experiments on Resume and Weibo data sets that the proposed method has better effect than the traditional method.