In order to improve the recognition accuracy of high-altitude nuts and reduce the false detection rate of bolts and nuts, a high-altitude nut recognition model based on improved YOLO V5 is proposed in this study. Firstly, a new attention mechanism EMA was added to the backbone network to integrate more information. Secondly, in order to enhance the network"s feature extraction capability, BiFPN was used to replace the PANet of the neck network. Finally, SIOU was used to replace the original loss function CIOU to accelerate the convergence of the model and improve its classification accuracy. The results show that the improved model has better performance than the original YOLO V5 model. The accuracy of the improved model increased by 0.92%. The recall increased by 0.16%. The average precision 1 (mAP:0.5) increased by 0.53%. And the average precision 2 (mAP:0.95) increased by 2.26%. An actual recognition comparison experiment between the improved model and the original YOLO V5 model is being carried out. The experimental results that the improved model has better recognition performance, which reduces the missed detection rate and the false detection rate, and improves the actual recognition rate. The improved model can well meet the recognition and image data acquisition of high-altitude nuts. And it also provide a data foundation for subsequent nut maintenance.