Abstract: Radar automatic target recognition (RATR) and inverse synthetic aperture radar (ISAR) technologies play a crucial role in modern electronic warfare and target detection. ISAR enables high-resolution imaging of fast-moving targets in complex environments, with wide applications in target recognition, tracking, stealth detection, and electronic countermeasures. As battlefield scenarios become increasingly complex and the diversity of targets grows, traditional recognition systems struggle with limited adaptability and the problem of catastrophic forgetting, making them inadequate for continuous updates and accurate identification. To address these challenges, this paper proposes a continual learning framework with enhanced anti-forgetting capabilities. By integrating exemplar replay and feature augmentation strategies, the proposed method alleviates the performance degradation caused by the imbalance between new and old classes. In addition, residual fitting and knowledge distillation techniques are employed to capture inter-class variations and reduce model complexity, enabling a better balance between stability and plasticity. This approach demonstrates strong potential in improving the adaptability and efficiency of radar target recognition systems, providing an effective solution for continual learning in dynamic and evolving battlefield environments.