The small target in infrared images has fewer pixels and lack of obvious feature details in complex scenes, which make it difficult to extract target features and usually has low detection accuracy. This paper proposes an infrared small target detection method based on attention mechanism under complex background. Based on YOLOv5 (You Only Look Once) network, SimAMC3 attention mechanism module is designed to optimize the feature extraction layer of the network. The target detection head is designed by adding a feature fusion layer to change the depth of feature extraction, a new weak target detection layer can be obtained, so that the shallow feature layer can better retain the spatial information of the weak target. Finally, the screening method of prediction box is improved to increase the detection accuracy of objects with close distance or overlapping. In the experiment, two SIRST infrared dim-small target image datasets are selected to label and train them. The experimental results show that compared with the original YOLOv5 algorithm, the improved algorithm improves the mean average accuracy (mAP) by 4.8% and 7.1%. It can effectively detect infrared dim-small targets under different complex backgrounds, reflecting good robustness and adaptability, so it can be effectively applied to detect infrared dim-small target.