The current named entity recognition task based on the pre-training-fine-tuning model has a gap between pre-training and fine-tuning， which makes it difficult to effectively model the relationship between entities and contexts， and the current Chinese named entity recognition methods cannot obtain sufficient character or word meanings. To address above problems， this paper proposes a named entity recognition method based on cue learning and incorporating multi-level feature information. Firstly， the cue text is constructed based on the cue learning mechanism， and then the character， word and entity-level feature information of the input text is spliced with it， which is taken as the input of the pre-trained model to effectively capture the semantic information between the contexts， narrow the gap between the pre-trained model and the downstream task， and improve the perceptive ability of the model for named entity recognition. The proposed method makes full use of prior knowledge to increase the learning ability of the model and improve the effectiveness of named entity recognition in the complex and variable semantic environment of Chinese. The F₁ values reach 97.09%， 96.68%， 83.44%， 97.48% and 76.05% on the People’s Daily， MSRA， Weibo， Resume and CMeEE datasets， respectively. Experimental results show that the proposed method is generally better than the current mainstream Chinese named entity recognition methods.