Continual learning in deep neural networks, in the context of video violence detection, faces the significant challenge of catastrophic forgetting, where a model loses previously learned knowledge upon encountering new classes. In this study, we address class-incremental learning for video violence detection and propose a framework for this unexplored domain. Initially, a 3D convolutional neural network model is trained to classify Normal and Violence classes, achieving high accuracy that reaches 99%. However, upon introducing a new class (Weaponized), the model demonstrates substantial performance degradation on the original classes, highlighting the impact of catastrophic forgetting. To mitigate this, a hybrid continual learning strategy, Continuous Class Conqueror (CCC), is proposed, which combines Learning without Forgetting (LwF) and Replay technique. Experimental results show that the Continuous Class Conqueror approach effectively preserves the model’s performance on previously learned classes, preserving accuracy up to 79% while allowing it to learn new classes with a high accuracy metric of 92% incrementally for video violence data, validating the importance of hybrid continual learning strategies.