When prototypical networks are directly applied to few-shot named entity recognition (FEW-NER), there are the following problems: Non-entities do not have strong semantic relationships with each other, and using the same way to construct the prototype for both entities and non-entities will make non-entity prototypes fail to accurately represent the semantic characteristics of non-entities; using only the average entity vector as the computing method of the prototype will make it difficult to capture similar entities with different semantic features. To address these problems, this study proposes a FEW-NER based on fine-grained prototypical networks (FNFP) to improve the annotation effect of FEW-NER. Firstly, different non-entity prototypes are constructed for different query sets to capture the key semantic features of non-entities in sentences and obtain finer-grained prototypes to improve the recognition effect of non-entities. Then, an inconsistent metric module is designed to measure the inconsistency between similar entities, and different metric functions are applied to entities and non-entities, so as to reduce the feature representation between similar samples and improve the feature representation of the prototype. Finally, a Viterbi decoder is introduced to capture the label transformation relationship and optimize the final annotation sequence. The experimental results show that the performance of the proposed method is improved compared with that of the large-scale FEW-NER dataset, namely FEW-NERD; and the generalization ability of this method in different domain scenarios is verified on the cross-domain dataset.