Object tracking by using single feature results in a poor performance in complex scene, and the traditional template update strategy is not robust to target appearance changes. Therefore, the paper presents a multi-feature fusion tracking method by mean-shift and particle filter tracking framework. The particles were optimized by mean-shift thus overcoming the degeneracy problem. According to the distribution of particles, the fusion coefficient of each feature is estimated online. Besides, a dynamic layered-weighting strategy is used to adjust the updating weights adaptively according to the variety of each component in template. The experimental results show that our algorithm can deal with dynamic appearance changes of target, thus it is suitable for tracking in dynamic scene.