To address the issues of low accuracy and high labor requirements in traditional fish hypoxia detection methods, a hypoxia risk assessment method for cultured fish based on Prune-YOLOv5s has been proposed. This paper introduces a hypoxia risk assessment method for cultured fish based on the Prune-YOLOv5s algorithm. This method firstly collects data on aquatic surface respiration (ASR) performed by fish under hypoxic conditions to create a data set for fish hypoxia. The dataset is then utilized to train the YOLOv5s model. Then, the lightweight and improved YOLOv5s model was used to monitor the behavior of fish surface respiration during hypoxia in real time. The introduction of the ASR coefficient allows for the quantification of ASR instances in fish, which is indicative of hypoxia risk. And the fish hypoxia assessment module is designed to evaluate the risk of hypoxia. The improved performance of the YOLOv5s model before and after modifications and the accuracy of the fish hypoxia assessment module are tested through the fish hypoxia experiment.The test results show that compared with the YOLOv5s model, the detection accuracy, model size, inference speed and detection speed of the PruneYOLOv5s model have been significantly improved. Among them, the detection accuracy of the 65% PruneYOLOv5s model, which has the best comprehensive performance, has been increased by 0.6% compared with the original model. The size of the model is reduced to 45.3% of the original model. The inference speed is improved by 23.8%, and the detection speed is also improved by 31.4%. The fish hypoxia assessment method achieves 97.4% accuracy in the test set of 39 test videos, and has a good performance in the hypoxia cycle experiment. The research indicates that the Prune-YOLOv5s-based hypoxia risk assessment method for cultured fish can effectively detect hypoxic conditions and provide accurate risk alerts, showing high feasibility for practical application.