Among various approaches of few-shot named entity recognition (NER), two-stage models based on prototype networks are widely used. However, these methods can not fully utilize the semantic information in entity labels and overly relies on entity type prototype vectors in distance calculation, resulting in poor generalization ability of the model. To address these issues, this paper proposes a few-shot named entity recognition method based on label semantic information awareness. This method consists of a two-stage process: entity span detection and entity type classification. When constructing entity type prototype vectors, the semantic information associated with the corresponding entity types is considered and fused with the prototype vectors through a dimension transformation layer. During the entity recognition of new samples, entity type positive and negative samples are combined with entity type prototype vectors to form entity type triplets, and the samples are classified based on the distance to the triplets. Experimental results on multiple datasets demonstrate that the proposed model significantly outperforms previous models.