Fine-grained named entity recognition is to locate entities in text and classify them into predefined fine-grained categories. At present, Chinese fine-grained named entity recognition only uses pre-trained language models to encode characters in sentences and does not take into account that the category label information can distinguish entity categories. Since the predicted sentence does not have the entity label, the associated memory network is used to capture the entity label information of the sentences in the training set and to incorporate label information into the representation of predicted sentences in this paper. In this method, sentences with entity labels in the training set are used as memory units, the pre-trained language model is used to obtain the contextual representations of the original sentence and the sentence in the memory unit. Then, the label information of the sentences in the memory unit is combined with the representation of the original sentence by the attention mechanism to improve the recognition effect. On the CLUENER 2020 Chinese fine-grained named entity recognition task, this method improves performance over the baseline methods.