Abstract: The aim of this work is to improve the hiding capacity of information hiding algorithms while ensuring the quality of generated text. To this end, this paper proposes a generative information hiding method based on couplet carrier. Firstly, we pre-train the couplet text data and build a couplet generation model based on a multi-flow pre-training and fine-tuning framework; secondly, we use the subject words as the input to generate the first line of a couplet, and the model can generate the first line of couplets on the same subject words; then we use the first line of a couplet as the input to generate the second line of a couplet. The algorithm mitigates the semantic ambiguity in the current couplet generation model by utilizing the Span-by-span learning approach, the padding generation mechanism and the noise perception mechanism to ensure that the generated couplets correspond to each other in terms of their metrical patterns. The secret information can be effectively hidden by different choices of subject words, candidate the first line of couplets and candidate words for generating the second line of a couplet. The experimental results show that the method can obtain high hiding capacity, and the average hiding capacity of 7-word couplets can reach 10.24 B, and the generated couplets satisfy the strict form and content requirements of couplets, such as equal number of words, comparable lexicality, proportional structure and harmonious ping-ze. The overall performance of the proposed method is better than the current mainstream generative text information hiding schemes.