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Precise feeding technology for fish ingestion is a key technology to achieve intelligent aquaculture. However, most of the precise feeding model is based on indoor aquaculture ponds with clear water quality, which are not suitable for outdoor open farming environments. In view of the actual situation, a set of detailed open pond dataset through water perspective acquisition was constructed, and the dataset was augmented to increase its diversity, and then the BiLSTM bidirectional recurrent neural network was embeded on the basis of the lightweight neural network MobileViT, so as to improve the memory ability of the model for video sequence data in a long period of time, and the CBAM attention module was combined with the MV2 module to design the CBAM-MV2 module, and then the CBAM-MV2 module was added to different layers of the model for experiments to obtain the most reasonable improvement scheme. Finally, an improved MobileViT-CBAM-BiLSTM fish feeding behavior classification model was proposed, which improved the prediction ability, robustness and generalization performance of the model, and realized the three classification of fish feeding behavior. The experimental results showed that the improved MobileViT was significantly better than previous in the collected video frame dataset, with an accuracy of 98.61%, 98.79% for Macro-F1, which was 6.33 percentage points for accuracy, 6.75 percentage points for Macro-F1 compared with the original MobileViT.