In order to enhance the retrieval accuracy of person re-identification, a soft mask foreground segmentation and multi information fusion re-ranking based person re-identification algorithm is proposed. A foreground segmentation algorithm based on U-Net is employed to remove the background information of the image while mitigating the sharp boundary transitions in the image segmentation to preserve the key information of the person in the image. In addition, a multi-information fusion re-ranking algorithm based on the siamese deep network is utilized to improve the quality of the retrieval results. Experimental results demonstrate that the two proposed methods are effective supplements to the person re-identification process and can be applied as effective methods to improve accuracy in most existing person re-identification algorithms.