Research on Optimization Algorithm of Japanese Language Translation System Based on Machine Translation Technology

This paper proposes an innovative optimization algorithm for Japanese language translation system based on context-adaptive vocabulary mapping and grammatical structure optimization. The algorithm optimizes the vocabulary mapping and grammatical structure in Japanese by combining deep learning technology, making the translation results more natural and fluent. Experimental results show that the proposed optimization algorithm improves the BLEU score by 15% and the translation speed by 20%. In the translation of common Japanese sentence patterns, the accuracy of the optimization algorithm is 18% higher than that of the traditional statistical translation model. In addition, when dealing with the translation of polysemous words and synonyms, the proposed algorithm can better understand the context and give more appropriate translation choices. Compared with the existing deep learning translation model, the algorithm in this paper shows stronger stability and higher translation quality when dealing with complex sentence patterns. This optimization algorithm can not only improve the accuracy and fluency of Japanese translation, but also has high computational efficiency, which is suitable for real-time applications in actual translation systems.