Addressing the low tracking accuracy of traditional correlation filter-based target tracking algorithms in scenarios involving fast target motion, occlusion, and complex backgrounds, an adaptive context-aware correlation filter target tracking algorithm is proposed. Based on the framework of correlation filter algorithms, the improvements mainly focus on mitigating the boundary effect caused by circular shifts and adjusting the fixed learning rate. Firstly, during the classifier training phase, an adaptive sampling strategy based on the extrema of the response map is introduced to incorporate contextual information. Then, a segmented learning rate adjustment strategy is adopted to make the algorithm better adapt to target changes. Finally, the performance of the algorithm is validated on standard datasets. The experimental results show that the proposed algorithm not only demonstrates good robustness in scenarios such as fast target motion, occlusion, and complex backgrounds, but also satisfies the requirement of real-time tracking speed. Furthermore, it can be integrated into most correlation filter-based algorithms as a framework.