To address the limitations of existing facial expression recognition methods that are restricted to basic expression categories and have difficulty adapting to new expression classes in dynamic scenarios, a composite facial expression recognition method based on incremental learning and multi-teacher knowledge distillation was proposed. An incremental learning framework was introduced to enable adaptive learning of composite expressions. To mitigate the issue of catastrophic forgetting commonly encountered in incremental learning, a selective clustering data replay strategy was designed to identify representative samples from previous classes, thereby maintaining the model’s retention of prior knowledge. Moreover, a confidence-aware multi-teacher knowledge distillation mechanism was developed, in which the predictions of teacher models are adaptively weighted according to their confidence levels with respect to true labels, ensuring efficient knowledge transfer. In addition, hierarchical knowledge distillation in the intermediate feature space was employed to enhance the student model’s fine-grained representation capability. Experimental results on the CFEE and RAF-DB benchmark datasets show that recognition accuracies of 78.52% and 74.23% are achieved, respectively, outperforming existing methods. The findings demonstrate that the proposed approach effectively alleviates catastrophic forgetting and exhibits superior performance in dynamic facial expression recognition tasks.