Application of Q-learning Virtual Network and Embedded Processor in Chinese English Translation Sentence Accuracy Analysis

Abstract

Abstract The widely available data is exploding with the continuous development of the Internet, leading people to the era of big data. Nowadays, massive amounts of English information can generate information redundancy, which to some extent affects the user's information browsing and filtering process, and Chinese-English translation needs to be further improved in accuracy. Therefore, it is very important to summarize the information in English source text and accurately translate it into Chinese to help people effectively and quickly grasp the key points and basic knowledge of English articles, among which satisfying sentence accuracy is the most important. Based on this point, this article proposes a new type of Chinese-English translation system, which is equipped with a Q-learning virtual network and built-in processor. With the development of recent years, language recognition technology has become increasingly mature. In a sense, this technology can be said to be the "cornerstone" of fully realizing artificial intelligence technology. Nowadays, the background of embedded processor based language recognition systems is microprocessor platforms (such as chips), which can create language training models in language databases, and can match the models with speech fragments to be tested to complete recognition. In particular, recognizing non specific language has evolved from a laboratory environment to become increasingly practical. This paper designs and implements a class of Chinese-English translation systems based on embedded processors and Q-learning algorithms to optimize system speed and achieve efficient and convenient system processing.