This study aims to construct a multi-stage rumor detection framework based on Retrieval-Augmented Generation (MS-RAG-RD) that enhances the accuracy and credibility of rumor detection. The MS-RAG-RD model achieved an F1 score of 91.3% and a robustness score of 94.97 for rumor detection performance. The model exhibited strong resistance to interference and low vulnerability to structured semantic perturbations in adversarial attacks. Ablation experiments validated the individual module contributions, emphasizing the critical role of the hybrid retrieval module in achieving performance gains. The study results indicated that the MS-RAG-RD model could enhance the performance of the rumor detection task, providing a technical framework for real-time monitoring and accurate tracing of misinformation in cyberspace governance.