Aiming at the problem that UAV target tracking is easily affected by factors such as the change of angle of view, occlusion and background clutter, a multi-feature spatial-temporal regularized correlation filters for UAV tracking is proposed.Firstly, the saliency feature is introduced into the spatio-temporal regularized correlation filter tracking framework, which is combined with the features of color, gray level and histogram of oriented gradient to improve the diversity of target appearance representation.Secondly, the peak sidelobe ratio is utilized as the weight to measure the peak strength of correlation response maps for different features, and the weighted features are combined for noise reduction, so as to realize the final weighted fusion in the response layer and improve the tracking accuracy.Finally, it is compared with 12 classic trackers on the public data set UAV123@10FPS.The experimental results and analysis show that the proposed method achieves favorable results in both tracking accuracy and success rate.