In order to solve the poor robustness problem of using single feature in the target tracking process, an adaptive fusing multi-features tracking algorithm is proposed based on the discriminability and stability of features in the particle filter framework. Several reliable features are adaptively selected by calculating their discriminative ability and stability, which are used to describe the target model, the multi-features fusion target model is established and the importance weights of features are set. In the process of state transition, a selective template updating method is presented based on the measurement of feature stability, and the occlusion problem is handled. Experimental results show that the proposed method can track the target under the complex scene in robust performance.