The rapid growth of social media has enabled rumors to spread swiftly through extensive online interactions， thereby significantly undermining public trust and destabilizing social order. However， existing rumor detection methods face notable limitations in modeling the dynamic semantic evolution of text and accurately capturing complex propagation patterns， and they often struggle to distinguish between ambiguous rumor categories. To address these challenges， we propose DySGCL （Dynamic semantic and graph feature fusion for contrastive rumor detection）， a novel contrastive learning framework that fuses dynamic semantic representations with graph-based structural features. Specifically， we employ a hierarchical Transformer to extract global semantic embeddings from users’ past posts， while a temporal convolutional module improves sensitivity to fine-grained semantic shifts. For structural modeling， we first simulate adversarial perturbations via edge removal， then leverage a graph attention network （GAT） to highlight critical interaction pathways in the propagation network. Finally， an integrated contrastive objective combining self-supervised and supervised signals further enhances the model’s discriminative power. Experiments on the Twitter15 and Twitter16 benchmarks show that DySGCL outperforms state-of-the-art baselines by 1.8% and 2.0% in accuracy， respectively， validating its effectiveness in dynamic and complex rumor detection scenarios.Highlights:1. A dynamic semantic and graph-structure fusion framework is proposed for rumor detection.2. A hierarchical Transformer and temporal convolution are integrated to capture semantic evolution in users’ historical posts.3. Self-supervised and supervised contrastive learning mechanisms are jointly used to improve the discrimination of ambiguous rumor categories.