Compared with traditional target tracking algorithms, the target tracking algorithm based on correlation filter has great advantages in tracking accuracy and real-time performance. However, when the target is blocked or out of view, it is difficult to extract target features and correctly locate the detection window, which may easily lead to target tracking failure or drift. Therefore, in this paper, a parallel correlation filter tracking algorithm based on confidence is proposed. First, the paper proposes a new confidence evaluation method to determine whether the target is blocked or out of view. Second, on the basis of confidence, two different trackers are fused with the combination weight of confidence to construct a parallel correlation filter tracking algorithm in order to improve the tracking accuracy and robustness. Finally, in order to prevent model pollution, an adaptive weight update strategy is adopted for the two filter models. Experiments on OTB-2013 and OTB-2015 datasets show that compared with traditional algorithms, this algorithm has significantly improved tracking accuracy and success rate.