Aimed at the varied envir onment and unstable object template in visual tracking,a classified sampling tra cking algorithm based on adaptive multiple features fusion is presented in this paper.The proposed method uses some overlapping sub-regions to divide the target model by the information of dense features,and each sub-region correspo nds to a multi-feature sampling window.The discriminative object template is construct ed by the adaptive multiple features fusion method,and this proposed strategy maximally improve the co mplementary of sub-region and the discriminability of template.The fusion strategy can improve the quality of measurement,and ensure sustainable and stable tracking in a particle filter fra mework. Experimental results show that the proposed method has good performance,and is more robust and stable to pose variation,dynamical background and occlusion than other methods.