The tracking of infrared small targets is one of the hot topics in infrared engineering, and it has wide applications in both civilian and military applications. However, due to the weak target, strong background, and dynamic changes of infrared small targets, tracking them remains a challenging task. The main challenge currently faced by infrared small target tracking algorithms is that: 1) The signal-to-clutter ratio (SCR) of infrared small target images is low, making the features easily overwhelmed. 2) Over time, changes in the scale and attitude of the target can affect tracking precision. 3) The presence of background clutter interference in the target environment affects tracking performance. Therefore, it is essential to establish a robust tracking method for infrared small targets.To address these issues and improve the robustness and accuracy of the tracker, this article proposes an adaptive tracking method for infrared small targets in dynamic and complex scenes. Firstly, in the feature extraction stage of infrared small targets, this study proposes a dual channel multi-scale feature extraction and fusion sub network based on the concept of twin networks. This sub network aims to minimize feature loss and solve the problem of low signal-to-noise ratio (SCR) in infrared small target images. Secondly, in order to mitigate the impact of target changes on tracking results, this study designed two modules. On the one hand, this article utilizes the spatial invariance of network models and proposes a dynamic template feature enhancement module (DTFE) in the template branch to enhance template features, thereby obtaining robust initial frame template features and effectively alleviating tracking failures caused by target changes. On the other hand, this article proposes an Adaptive Template Update Module (ATU), which adaptively updates template features using information from historical frame targets, alleviating the impact of target scale changes on tracking results. Finally, this article proposes a multi-layer self attention module (MSA) in the tracking branch, which utilizes attention mechanisms to reduce the interference of background clutter.The success rate (Tab.1) and accuracy (Tab.2) of the method proposed by our research institute on publicly available infrared small target test sequences are superior to most advanced methods. Its average success rate and average accuracy reached 85.5% and 91.5%, respectively. The success rate curve (Fig.4) and accuracy curve (Fig.5) of the method proposed in this article are superior to several advanced methods. The several modules proposed in this study have also undergone sufficient ablation experiments, and the experimental results (Tab.3, Tab.4) show the contribution of the multi-scale feature extraction and fusion sub network and DTFE, MSA.ATU proposed in this paper to the target tracking results.This article proposes an infrared small target tracking method based on feature enhancement and multi attention mechanism to solve the problems caused by weak targets, strong background interference, and dynamic changes of targets. This study proposes a dual channel multi-scale feature extraction and fusion sub network to address the problem of low signal-to-noise ratio in infrared small target images. The proposal of DTFE, MSA, and ATU modules aims to effectively alleviate the impact of strong background and dynamic target changes, thereby enhancing the robustness of tracking infrared small targets. The experimental results on the infrared small target public test sequence show that compared with several advanced methods, this method has better success rate and accuracy, while meeting real-time requirements in terms of speed. In future work, we are committed to further enhancing the tracking capability of infrared small targets. Subsequent research will include simplifying the model to make it lightweight and exploring methods for predicting the position of occluded targets.