With the rise of deep learning， person re-identification has gradually become a hot topic in the computer vision field. It performs cross-camera retrieval through a given query image， and finds the images that match the query identity. However， due to the factors such as background and illumination under different cameras， there are a large number of hard negative samples in the collected pedestrian datasets， and the performance of the model trained using these samples is bad and lacks robustness. Therefore， in order to improve the ability of the model to discriminate such negative samples， a novel method of synthesizing images with hard negative samples information through confusion factors is designed. For each input batch images， the similarity measurement is used to find the hard negative sample corresponding to each image， the new generated images with the clues of negative samples are synthesized through the confusion factor， and the model is prompted to mine the negative samples information in a supervised manner thus improving the model robustness. A large number of comparative experiments show that the proposed method achieves high performance on the mainstream datasets. The ablation study proves the effectiveness of the proposed method.