To cope with the problem of object tracking failure in the challenging environment, a target tracking method based on multi-correlation filter combination was proposed. Firstly, two kernelized correlation filters（KCF） based on color name（CN） features and histogram of oriented gradient（HOG） features, respectively, integrated the map information through adaptive fusion method, and were used to determine the prediction position of the target. Then, through the multi-scale sampling based on the target region, CN-HOG compositive feature was extracted to construct a scale correlation filter to obtain the optimal scale of target. Finally, the adaptive updating strategy of the model was designed to determine whether the model was updated in the current frame through determining whether the target was occluded. The proposed algorithm and 6 state-of-the-art methods were tested on 50 video sequences. The experiment results indicate that the proposed algorithm gains the best precision and success rate in the challenging environment, it can effectively deal with the problem of object occlusion and scale change, and it has a fast tracking speed.