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In order to solve the problem that the particle filter(PF) algorithm based on multi-feature fusion under complex environment did not offer a high accuracy of tracking technique, an improved multi-feature fusion algorithm was proposed. The research proposed the second-order central difference Kalman filter(SO-CDKF) to generate the proposal distribution function which can match the true posterior distribution more closely. The latest observation information was fused into the importance sampling to improve the efficiency of particles. Meanwhile, the introduction of template updating strategy was combined to update target template in real time. In multi-feature fusion strategy, Expectation-Maximization(EM) algorithm based on PF framework was used to get the state estimation of different quantity of particle sets, so as to avoid errors caused by calculating the weights of multi-feature, and improve the real-time performance. Filter simulation results show that the proposed method has the best performance compared with the other improved PF algorithms in one-dimensional nonlinear model. In the experiment of target tracking based on video sequence, the effec-tiveness of the proposed algorithm is verified by comparing it’s performance of different features and quantity of particles. Finally, a series of tracking experiments in different environments show that the pro-posed algorithm has high accuracy and robustness for target tracking under complex conditions．