Data mining based on computer game search algorithm in Japanese E-learning and multi context translation

Abstract

With the continuous expansion of the translation market, users’ demand for translation tools such as computer software is also increasing, resulting in various machine translation technologies and methods. Japanese translation, as one of the important components of Chinese publicity work, has strong professionalism and complexity. In practice, how to effectively improve the translation quality of Japanese translation models using computer technology has always been a research issue of great concern. Based on this, we propose a game search algorithm and use it as a prototype to improve the algorithm. Then, a data mining model was established that can effectively improve search performance. This model solves the above problems by introducing genetic algorithms and dynamic programming theory. At the same time, in order to further improve the efficiency of existing data mining algorithms, we need to explore more efficient methods and strategies. After experimental verification, the four data mining models proposed in this article perform the best in numerical fitting. Overall, in Japanese multi context translation systems, the use of data mining techniques based on computer game search algorithms can provide more accurate and matching related translations. Compared to traditional Japanese translation software, it has higher quality and efficiency.