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The recognition of fish species holds significant importance in aquaculture and marine biology. However, it is a challenging problem due to the high similarity among intra-genus species. Existing recognition methods primarily seek prominent features of the species. However, we believe that the diverse levels of similarity between a species and other species can also function as implicit characteristics for that specific species. Based on this perspective, we propose a dual-branch fusion network for fine-grained fish species recognition utilizing inter-species similarity. This approach consists of a backbone network and two branches for coarse- and fine-grained recognition. In the coarse-grained branch, we designed a guidance matrix and species similarity labels to facilitate the generation of species similarity information. In the fine-grained branch, features from the backbone network are fused with similarity information to achieve precise recognition. Finally, fine-tuning the neural network through loss functions. We conduct experimental validation on three publicly available fish datasets, yielding excellent accuracy outcomes. Code is available at https://github.com/xingxing317/fish_classification. [ABSTRACT FROM AUTHOR]