In order to accurately identify the fall posture of the elderly, an improved YOLOv8s fall detection model is proposed. Firstly, the SE attention mechanism module is introduced into the backbone network of YOLOv8s model, which divides the channel features into multiple sub-graph features, and fuses the features of different groups, so that the network can adaptively focus on the key features, suppress the features that contribute less to the current task, and improve the feature extraction ability; Secondly, the CIoU loss function is replaced by EIoU to accelerate the convergence rate and improve the accuracy and stability of the model. Finally, the trained model is verified on URFD and other data sets. The experimental results show that the precision rate of the model reaches 99. 50%, the recall rate reaches 99. 00%, and the mAP50 reaches 99. 50%, which is better than the original model. Compared to the YOLOv5s+K-means++ model, the proposed model exhibits an increase of 3.22% in accuracy, 5.32% in recall, and 2.38% in mAP50. When compared with the C2D-YOLO model, improvements are seen with a 10.00% increase in accuracy, 11.40% in recall, and 7.80% in mAP50. Furthermore, against the YOLOv5s+C3new model, the improvements are 2.50% in accuracy, 6.80% in recall, and 4.1% in mAP50.. The improved model has greater advantages than the original model and the current advanced model.