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In order to achieve real-time detection of fall behavior among power plant personnel and prevent the occurrence of events that cannot be detected and rescued in a timely manner due to falls and coma, an improved YOLOv5s fall behavior detection algorithm network is proposed to address the issues of insufficient real-time detection and feature extraction capabilities. The introduction of SKAttention module in the YOLOv5s model enables the network to automatically utilize the information captured by effective receptive fields for classification.This new deep structure allows CNN to perform dynamic selection mechanisms on the convolutional core, thereby adaptively adjusting the size of its receptive field; By combining ASFF adaptive spatial fusion and fully utilizing different features, and introducing weight parameters into the algorithm, based on multi-level functions, the goal of improving the accuracy of underwater target recognition is achieved;The addition of spatial pyramid pooling structure SPPFCSPC greatly reduces inference time. The experimental results show that compared to the original YOLOv5s, the new network has improved the average accuracy of mAP by 2.1% and the recall rate by 16%. The improved network is more powerful in perception of details and spatial modeling, and can more accurately capture abnormal behaviors of people falling, significantly improving the detection effect.