Abstract: Abstract: The vast number of marine organisms，coupled with the high degree of morphological similarity among different species，poses challenges for their identification and classification.Current research methods primarily utilize convolutional neural networks and self-attention mechanisms to extract features and directly perform classification. However，this approach overlooks the potential hierarchical structure that may exist among categories.To address this issue， a novel hierarchical classification algorithm is proposed， which integrates convolution and self-attention mechanisms.This method fully exploits the advantages of convolution in capturing local features at shallow layers and self-attention in capturing global features at deeper layers，naturally combining the two. Additionally，based on prior biological knowledge，we construct a hierarchical structure among marine organism categories and create branches at both deep and shallow levels to utilize hierarchical relationships for predictions ranging from coarse to fine categories.To further enhance the interaction between deep and shallow layer information，we propose a dynamic connectivity pattern， enabling the network to obtain information of different granularity across different hierarchical levels. Finally，we introduce a category relation enhancement module at the end of the network to assist the network in learning hierarchical semantic relationships，thereby achieving more accurate classification. Experimental results demonstrate that the proposed algorithm outperforms existing classification methods.

Key words: Key words: image recognition, hierarchical classification, convolutional, attention mechanism, marine biology
E-F-MBConv