[Purpose/significance] This article aims to explore whether integrating different semantic knowledge into machine translation models can enhance the effectiveness of machine translation and which type of semantic knowl⁃ edge plays a more significant role. The purpose is to support the research in machine translation and the inheritance and dissemination of Chinese excellent traditional culture. [Method/process] The study selected 300,000 pairs of me⁃ ticulously processed "Ancient Chinese-Modern Chinese" parallel corpora from the "Twenty-Four Histories" as experi⁃ mental data. Based on the neural machine translation model OpenNMT, it integrated word boundary knowledge, partof- speech knowledge, entity knowledge, and dependency syntax knowledge into the training process of the machine translation model through three different feature fusion methods. [Result/conclusion] The integration of different se⁃ mantic knowledge with the model has varying impacts on the translation effectiveness of classical texts. Word boundary knowledge, part- of- speech knowledge, and entity knowledge contribute to the machine translation task, with entity knowledge making the largest contribution, while the role of dependency syntax knowledge has no obvious effect.