Recently, point cloud semantic segmentation technology has made significant progress, boosting the development of autonomous driving, robotic navigation, and urban modeling. However, most current approaches rely on training data of all categories at once. This limitation makes it difficult for models to adapt to dynamic environments, leading to repetitive retraining and high computational costs. To enable continuous learning of new categories by leveraging previous knowledge, and inspired by the brain’s ability to learn new knowledge through comparison and association, we introduce a cross-generational contrastive continual learning approach for 3D point cloud semantic segmentation. To mitigate catastrophic forgetting, we contrast representations of old classes and new classes across different generations of encoders. Further, we propose a refined labels guided contrastive loss, which comprehensively accounts for the semantic dependencies between points and leverages previous knowledge. Additionally, we propose a refined label estimation strategy to boost the confidence of all classes while retaining previous knowledge. Extensive experiments on two public 3D point cloud semantic segmentation benchmarks demonstrate the effectiveness of our proposed approach.