The emergence of multimodal technologies has propelled Vision-Language Incremental Learning (VLIL) into a research spotlight. Current VLIL approaches predominantly inherit unimodal paradigms, failing to address fundamental distinctions between visual and linguistic modalities. Crucially, the semantic gap between images and text creates divergent learning dynamics: visual data exhibits rich, distributed information while textual representations remain explicit and compact. Consequently, textual elements align with class-specific tasks, whereas individual images inherently span multiple such tasks, creating dual bottlenecks in class-level memory allocation and scene-level knowledge transfer. To overcome these challenges, we propose ​DCIM (Dual Class-Individual Memory)​, a novel framework featuring complementary mechanisms for vision-language continual learning. For class-level constraints, our ​Hierarchical Class Memory Management (HCMM)​​ strategy dynamically allocates memory resources across object categories. It employs forgetting simulation to identify and preserve the most vulnerable samples, ensuring robust long-term knowledge retention. For scene-level adaptation, the ​Scene Reconstruction Memory(SRM)​​ module captures generalized environmental representations, enabling contextual transfer to novel classes and disambiguation of semantically related concepts within shared scenes.Extensive experiments on two vision-language tasks, i.e., visual question answering (VQA) and Image captioning (IC), demonstrate the effectiveness and excellent generalization ability of our approach, achieving superior performance under continual learning settings.