In increment learning, as the number of tasks increases, the knowledge learned by the model on the old task is catastrophically forgotten after the model is trained on the new task due to a series of problems such as step-by-step data migration, resulting in the degradation of the model performance on the old task. Given this problem, a class-incremental learning method based on knowledge decoupling is proposed in this study. This method can learn the common and unique knowledge of different tasks hierarchically, combine the two kinds of knowledge dynamically, and apply them to the downstream classification tasks. Besides, the mask strategy of the natural language model is used in replay learning, which prompts the model to quickly recall the knowledge of the previous tasks. In class-incremental experiments on NLP datasets—AGNews, Yelp, Amazon, DBPedia and Yahoo, the proposed method can effectively reduce the forgetting of the model and improve the accuracy and other indicators on various tasks.