Concerning the problem that existing target tracking algorithms mainly use the linear constraint mechanism LADCF （Learning Adaptive Discriminative Correlation Filters）， which easily causes model drift， a correlation filtering based target tracking algorithm with nonlinear temporal consistency was proposed. First， a nonlinear temporal consistency term was proposed based on Stevens’ Law， which aligned closely with the characteristics of human visual perception. The nonlinear temporal consistency term allowed the model to track the target relatively smoothly， thus ensuring tracking continuity and preventing model drift. Next， the Alternating Direction Method of Multipliers （ADMM） was employed to compute the optimal function value， ensuring real-time tracking of the algorithm. Lastly， Stevens’ Law was used for nonlinear filter updating， enabling the filter update factor to enhance and suppress the filter according to the change of the target， thereby adapting to target changes and preventing filter degradation. Comparison experiments with mainstream correlation filtering and deep learning algorithms were performed on four standard datasets. Compared with the baseline algorithm LADCF， the tracking precision and success rate of the proposed algorithm were improved by 2.4 and 3.8 percentage points on OTB100 dataset， and 1.5 and 2.5 percentage points on UAV123 dataset. The experimental results show that the proposed algorithm effectively avoids tracking model drift， reduces the likelihood of filter degradation， has higher tracking precision and success rate， and stronger robustness in complicated situations such as occlusion and illumination changes.