Radio-Frequency (RF) based User identification enables many attractive applications such as smart homes, and security management. However, laborious data collection is required due to appearance changes, inconsistent walking paths, and environmental variations. Furthermore, multi-user identification persists as an imperative for real-world applications. To this end, we propose an RFID-based user identification system (RF-UI), a few-shot, cross-interference factor, and a continuous user identification system. To begin with, we construct a Joint Similarity Matrix (JSM) for characterizing gait features that remain robust across various interference factors. Subsequently, RF-UI achieves cost-effective data augmentation by deploying just a few additional tags. Finally, to achieve continuous user identification, RF-Ul has developed a continuous user motion segmentation and recognition method. It utilizes phase energy fluctuation and neighborhood energy sliding windows to segment different users' walking motions and extract valid segments. These segmented motion clips are then input into a cubic SVM classifier to identify users. Extensive experiments have validated that RF-UI can achieve reliable identification across varying interference factors and effective continuous user identification.