The research of Chinese and Japanese machine translation technology is of great practical significance for promoting and inheriting Chinese excellent culture and advancing the development of economy, education, and culture in China and Japan. In this paper, a cross-lingual keyword extraction machine translation model is investigated. The traditional model is improved on the basis of recurrent neural networks RNN and attention mechanism, and the problem of low information memory capacity of LSTM and GRU algorithms is optimized. By introducing the self-attention mechanism, important information can be filtered from a large amount of information, and a keyword recognition system can be constructed. The system covers three levels: word embedding layer, encoding layer, and CRF layer, and the model effectively utilizes the feature extraction mechanism of the Transformer model and introduces the bidirectional mechanism to obtain the richer semantic representation of word vectors. After the above strategies, the scores on the task of extracting the keywords of the discourse system with Chinese characteristics reach up to 89 in word inference and 83 in cultural adaptation, which effectively improves the performance of machine translation compared with other neural machine translators. The keyword “community of destiny” appears frequently in Japanese media reports, and the content involves the excellent cultural essence of the discourse system with Chinese characteristics, which has a benign influence on the dissemination process in Japanese society.