Unsupervised domain adaptation aims to transfer the source domain knowledge to the target domain dataset, which is a very important task in person re-identification. In reality, the collected data often has video frame information. Therefore, it is very easy to obtain pedestrian labels from a single camera view in the target domain dataset. However, the existing methods ignore the available information, which limits the improvement of recognition performance. For this reason, knowledge guidance and fine-grained information enhancement for unsupervised domain adaptation person re-identification is proposed. In this method, a new paradigm with a single-view pedestrian sample known in the target domain is developed to fully mine the useful information in the target domain. At the same time, the source domain knowledge is used as a guide to assist the model to extract the discriminative features of pedestrian images in the target domain. Compared with the existing knowledge transfer strategies, this knowledge-guided approach can effectively alleviate the domain shift. Furthermore, the local information of pedestrians is embedded into the global feature, and the fine-grained information of the feature is enhanced. Experiments on two publicly datasets fully prove the effectiveness and superiority of the proposed method. Code: https://github.com/lhf12278/KG-FGIN