With the rapid development and expansion of global aquaculture and the diversification of farming models, the scale, intelligence, and informatization of the aquaculture industry have become trends in its development. Fish behavior recognition is of significant importance in ecology, aquaculture, and fisheries resource management. It enables the assessment of fish growth, developmental status, and activity levels based on their behavioral patterns, indirectly evaluating the impact of environmental factors. This can help reduce stress responses in fish growth, improve resource utilization efficiency, and lay the foundation for intelligent development in aquaculture. Traditional fish behavior identification mainly relies on manual observation and recording, which consumes a considerable amount of time and effort and is subject to subjectivity and uncertainty. In recent years, fish behavior recognition methods based on artificial intelligence get extensive attention, is lossless, such as low cost advantage. The fish behavior recognition technologies were reviewed based on artificial intelligence over the past five years, including convolutional neural networks, recurrent neural networks, and two-stream convolutional neural networks. It also provided a summary and analysis of fish behavior recognition methods and datasets. Based on these foundations, an outlook on future research directions was discussed and provided.