Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain, playing a very important role in person re-identification. In real-world applications, video-based pedestrian data are often available, making it feasible to obtain single-camera-view labels in the target domain. However, existing UDA methods typically ignore this readily accessible information, thereby limiting performance improvements. To address this issue, we propose a knowledge-guided and fine-grained information enhancement framework for UDA person re-identification. A novel paradigm is introudced that leverages single-view labeled pedestrian samples in the target domain to fully exploit intra-domain information. Meanwhile, source-domain knowledge is used as guidance to assist the model to extract more discriminative target-domain pedestrian representations, effectively mitigating domain shift compared with conventional knowledge-transfer strategies. Furthermore, local pedestrian cues are integrated into global features to strengthen fine-grained feature expression. Experiments conducted on two publicly datasets fully demonstrate the effectiveness and superiority of the proposed method.