Infrared small target detection has a wide range of applications in the military, security and aerospace fields. In these applications, small infrared targets are typically characterized by small target sizes, low signal-to-noise ratios, and a lack of texture features and shape features, posing significant challenges for target detection. The existing infrared target detection methods rely on complex feature extraction steps or unable to effectively extract information from small targets, making it difficult to achieve ideal detection results. In order to solve the above problems, an infrared small target detection model based on a nested network and an attention mechanism is proposed. Firstly, a nested network structure is utilized to effectively extract the multi-scale features of the image. Then, an attention mechanism is introduced to enhance the target area information and suppress the background information, so as to improve the perception ability of small targets in the infrared image and improve the accuracy and robustness of target detection. Experimental results show that the proposed method has significant advantages in terms of detection rate and positioning accuracy, and is of positive significance for further research in the field of infrared image processing.