Design of Corpus Translation Data Identification System that Integrates Multi-source Information

With the acceleration of globalization and the increasing multilingual communication, the demand for text translation is increasing. In order to improve the quality and efficiency of translation, traditional translation systems cannot meet the requirements of information processing in complex contexts. Therefore the purpose of this study is to design an efficient translation data recognition system to facilitate efficient and accurate communication between different languages. Based on the deep learning algorithm, this paper constructs a multi-source corpus and combines the natural language processing technology to realize the intelligent recognition and automatic retrieval of translated data. Experimental results show that the efficiency of translation retrieval is improved to 89.1% on the premise of guaranteeing translation efficiency and accuracy. The results show that the design of integrated multi-source information can significantly improve the comprehensive performance of the system, thus providing users with more convenient and high-quality translation services.