The Role of Artificial Intelligence in the Core of Intelligent Translation Systems

Nowadays, traditional translation systems still have shortcomings in handling complex grammatical structures, semantic understanding, and cross-cultural context conversion. To address these issues, this study introduces artificial intelligence (AI) technology into the core of intelligent translation systems, and constructs an efficient and accurate intelligent translation system through deep learning (DL), natural language processing, and big data (BD) analysis methods. This study explores how to utilize DL models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to enhance the language processing capabilities of translation systems. In terms of method implementation, we have developed a translation framework based on the Transformer model, with an average satisfaction score of 4.2, indicating that the majority of users are satisfied with the performance of the translation system. This study not only provides new ideas and methods for the development of intelligent translation technology, but also provides effective solutions for language barriers in cross language communication and globalization processes.