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As false information continues to proliferate across social media platforms,effective rumor detection has emerged as a pressing challenge in naturallanguage processing. This paper proposes RAGAT-Mind, a multi-granular modelingapproach for Chinese rumor detection, built upon the MindSpore deep learningframework. The model integrates TextCNN for local semantic extraction,bidirectional GRU for sequential context learning, Multi-Head Self-Attentionfor global dependency focusing, and Bidirectional Graph Convolutional Networks(BiGCN) for structural representation of word co-occurrence graphs. Experimentson the Weibo1-Rumor dataset demonstrate that RAGAT-Mind achieves superiorclassification performance, attaining 99.2% accuracy and a macro-F1 score of0.9919. The results validate the effectiveness of combining hierarchicallinguistic features with graph-based semantic structures. Furthermore, themodel exhibits strong generalization and interpretability, highlighting itspractical value for real-world rumor detection applications.

RAGAT-Mind: A Multi-Granular Modeling Approach for Rumor Detection Based on MindSpore