To address the difficulty of balancing stability and plasticity in the existing continual learning methods， a multi-model collaborative continual learning framework based on filtering mechanism was proposed. The framework is composed of a main model， an auxiliary model， and a student model. The main model was designed to focus on predicting and outputting high-confidence results， the auxiliary model was used to memorize hard samples that the main model failed to learn， and the student model was employed to inherit and integrate the recognition ability of a cascaded teacher model constructed from the main and auxiliary models. The three models were optimized collaboratively during each task process. Experimental results show that in task-incremental scenarios， the proposed framework achieves higher AAC （Average ACcuracy）， BWT （BackWard Transfer）， and FWT （ForWard Transfer） than typical methods such as EWC （Elastic Weight Consolidation）， GEM （Gradient Episodic Memory）， and iCaRL （incremental Classifier and Representation Learning） on both the Fashion-MNIST and CIFAR-10 datasets， demonstrating stronger stable memory and adaptive capabilities； in class-incremental scenarios， the framework has good applicability on both the 10-class Fashion-MNIST and the 100-class CIFAR-100 datasets， with its AAC higher than that of the comparative methods significantly.
