In the target tracking, the tracking efficiency is effectively improved after introducing regularization in the tracking algorithm based on the correlation filter, but it spends a lot of energy adjusting the predefined parameters. In addition, the target response occurs in non-target areas will lead to tracking drift. Therefore, a context-aware correlation filter algorithm based on time and automatic spatial regularization is proposed. First, during the tracking process, the automatic spatial regularization is realized using the target local response change, adding the automatic space regularization module to the target function to enable the filter to focus on the learning of the target object. Second, the tracker makes full use of the global context information of the target, combined with the automatic spatial regularization, to enable the filter to learn more information about the target in time, and reduce the impact of the background on the tracking performance. Subsequently, we add a temporal awareness to the filter to fully learn the change of targets between adjacent frames to obtain more accurate model samples. Experimental results show that the algorithm has better tracking effect in distance accuracy and success rate when comparing to other tracking algorithms.