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Based on convolution neural network, this paper proposed a new asymmetric two branch underwater biological classification model to solve the problems of low visibility, poor illumination conditions and no obvious differences among species in the underwater environment. In the model, the interactive branch used different convolution neural network to extracted local features and interacted with local features through the interactive module to enhance the classification model. Convolutional neural network branch could effectively learn the global characteristics of the target and made up for the global information ignored in the interactive branch. Finally, this model obtained 98 . 9%, 98 . 3% and 97 . 9% of the accuracy on the three data sets of Fish4-Knowledge (F4K), Eilat and RAMAS, which were significantly improved compared with the previous methods. Visual interpretation also verifies that the model could effectively capture local features and eliminated the background influence. Finally, it shows that the model has good classification performance in underwater environment. [ABSTRACT FROM AUTHOR]