Accurately and efficiently monitoring the stress behavior of fish fry not only helps to regulate stressors during the breeding process to reduce yield losses, but also provides an effective means for evaluating the vitality of fish fry during the breeding stage. In view of the characteristics of fish fry, such as small size, high stocking density, and high - speed non - linear movement, this study proposes a method for monitoring the stress behavior of fish fry by improving YOLOv8n - pose and combining it with BoTSORT.The improved YOLOv8n - pose is used as a detector. The BMS module is combined with the C2f module to enable the model to fully learn features at different scales. The SPPCSPC module is used to replace the original feature fusion module of the model to optimize the detection accuracy in the case of fish fry occlusion. Finally, N - EMASlideLoss is used to replace the original loss function of the model, enhancing the model's stability and attention to small targets.In the tracker part, based on the targets detected by the detector, a method more suitable for monitoring the non - linear movement of fish fry under stress is achieved by combining the BoTSORT multi - target tracking algorithm.Finally, three features of fish fry, namely acceleration, tail - wagging angle, and aggregation degree, are extracted and weighted for fusion. Based on the fused feature values, it is determined whether the fish fry are under stress. The experimental results show that the mAP of the improved YOLOv8n - pose algorithm in target detection and key - point detection is 3.6% and 4.5% higher than that of the original model respectively. The MOTA of the BoTSORT algorithm is 77.628%, the MOTP is 80.307%, the IDF1 is 79.573%, and the IDSW is 51, which are superior to those of the DeepSORT, ByteTrack, and StrongSORT algorithms. The accuracy of the stress behavior monitoring of this study's algorithm based on feature values is 95.24%, providing new ideas and methods for stress behavior monitoring in fish fry breeding.