Dynamic graph representation learning captures time-evolving topological structures and interaction patterns among entities, providing spatiotemporally aware embedding for downstream tasks such as link prediction, revealing the dynamic evolution patterns of complex systems. Continuous-time dynamic graphs, owing to their rich fine-grained temporal information, provide a more realistic modeling paradigm for complex processes such as social network evolution. However, current continuous-time dynamic graph representation learning faces at least three challenges. (1) The effective extraction of critical information from long historical interaction sequences requires robust modeling of long-term temporal dependencies. (2) Handling long interaction sequences requires controlling computational complexity to improve efficiency. (3) Traditional approaches that capture interaction patterns in chronological order struggle to reveal hidden non-causal associations. To address these challenges, this study proposes a novel continuous-time dynamic graph representation learning model. By leveraging a bidirectional selective state-space encoding mechanism, long-term temporal dependencies in interaction sequences can be captured, and subsequent interaction information can also be incorporated through backward pathways. The temporal unidirectional constraint is thus broken, enhancing the model’s global context comprehension capability. Extensive experimental results demonstrate that the model consistently outperforms baseline methods in predictive performance on real-world datasets spanning diverse domains, while maintaining high computational efficiency, enabling the modeling of long-term bidirectional dependencies under limited computational resources.