Recently, Discriminative Correlation Filter (DCF) based methods have been widely applied in tracking for unmanned aerial vehicles (UAVs) because of their promising performance and efficiency. How ever, boundary effect, filter corruption, lack of context information and the poor representation of the object lead to the decrease in discriminability. In this paper, a novel learning adaptively context weight-aware correlation filters with robust spatial-temporal regu larization method (ACRST) is proposed. Both convolutional features and hand-crafted features are employed to improve representa tions for object appearances. Then the ACRST tracker extracts context samples around the object to help the filter be aware of the background information and adaptively learns the weights of these context patches. Thus, the tracker can improve the robustness against background noises especially for similar samples. Mean while, the tracker merges a robust spatial-temporal regularization to prevent the filter corruption and boundary effect. We design a center-attention spatial regularizer to focus on the valid informa tion of the object better and we propose a method to obtain the value of the parameter of the temporal regularization adaptively. Ex tensive experiments have been conducted on 123 challenging UAV tracking sequences. The results prove that our tracker performs better than other state-of-the-art trackers