In order to solve the problem of  low recognition rate and slow recognition speed in pedestrian identification, we propose a dynamic weighted average ranking pedestrian identification method based on adaptive feature selection to solve the problem of pedestrian recognition. Firstly, the GrabCut algorithm is combined with the manifold-based saliency detection algorithm to improve the accuracy of pedestrian appearance extraction. Then, we propose an adaptive selection method to effectively extract pedestrian features. Finally, we design a dynamic weighted average ranking model to merge multidimensional dynamic features. Experimental results show that the proposed method can improve the accuracy of pedestrian recognition, and has good robustness to the change of attitude.