Abstract: Supervised machine translation based on deep learning has achieved good results, which it requires a large amount of high-quality aligned parallel corpora for training. For the Chinese historical and modern translation scenario, the relative scarcity of high-quality parallel corpora highlights the significant research value and necessity of text alignment in this field. In traditional research on bilingual sentence alignment, conventional methods use features such as length, vocabulary, and co-occurring characters in bilingual texts to establish a comprehensive evaluation criterion for measuring the similarity between two sentence pairs. Such methods have limited ability to match sentence semantics and perform poorly in the many-to-many alignment mode. This paper proposes using pre-trained language models with powerful semantic capabilities and reinforcement learning training objectives based on dynamic programming algorithms to integrate paragraph-level global. Experimental results demonstrate that the model trained using the method proposed in this paper outperforms the previously best-performing baseline models, with particularly significant improvements in handling many-to-many alignment patterns.