To extract rich entity information and normalized expressions from biomedical literature, this study proposes a multi-granularity feature fusion approach for biomedical named entity recognition and normalization (MGFFA). By integrating character-level, word-level, and concept-level textual information, the model significantly enhances its learning capability. It also incorporates a memory bank for storing and synthesizing information from different levels to achieve a deeper understanding of the complex relationships between entities and their normalized labels. With the integration of pre-trained models, MGFFA captures not only coarse-grained semantic representations of text but also conducts detailed analysis at the morphological level, thereby comprehensively improving the recognition accuracy of long-span entities. Experimental results on the NCBI and NC5CDR datasets demonstrate that the model outperforms other baseline models overall.