Design of the Intelligent Recognition Model for English Translation Based on the BP Neural Algorithm

• Published in: Security and communication networks Vol. 2022; pp. 1 - 12
• Main Author: Wang, Zhan
• Format: Journal Article
• Language: English
• Published: London Hindawi 06.10.2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Artificial intelligence; Back propagation networks; Combinations (mathematics); Computers; Control algorithms; Design; English language; Machine learning; Machine translation; Neural networks; Permutations; Recognition; Software; Students; Teaching; Translations; China
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2022/3799061

With the development of modern intelligent recognition, many intelligent recognition translation tools have emerged. These translation tools mainly include machine learning, neural network, KNN, and other artificial intelligence technologies. These technologies have been applied to many fields. Among them, machine translation is the most important and widely used one. A large number of English translation technologies have appeared in this development era. However, the translation accuracy of intelligent recognition technology cannot be guaranteed. Under the background of this English translation environment, we design an intelligent recognition algorithm of English translation based on the BP neural algorithm to improve the rationality of English translation and analyze the intelligent recognition model of English translation. The following conclusions are drawn from the experimental comparison Four Methods of Translation. (1) Compared with some similar algorithms, machine translation based on the BP neural algorithm has many characteristics, such as convenience, which are very suitable for English translation. (2) The intelligent recognition model of English translation using the BP neural network makes the sentence flow higher, which can solve some problems in translation and achieve coherent translation in context. (3) The intelligent recognition model with the BP neural network as the core realizes a variety of permutations and combinations of different characteristics of complex English sentences, solves many poor English sentences, and significantly improves the accuracy of English translation.