The Feasibility of the Intelligent Development of Contrastive Translation between English and Chinese based on the Development of E-commerce

Abstract

Objectives: At present, with the rapid development and application of the Internet, the cross-border transaction of e-commerce presents a blowout development, and the demand for language is increasing. Methods: In this paper, starting from the perspective of machine intelligent translation of English and Chinese, and in view of the problem of traditional contrastive translation of machine, the algorithm of strengthening neural network was used to solve the problem of translation. In the study, the process of intelligent translation was divided into two stages: encoding and decoding. In view of the language type and word alignment, the input and output modules were formed and the algorithm was optimized, and a recurrent neural network algorithm was used to build an RNN-embed intelligent translation model of English and Chinese. Results: The model was input through the character level in English and Chinese, and then the network was trained, so as to solve the problem that it is difficult to deal with the advanced semantics in the process of strengthening the neural network calculation of text information in the cross-border transaction of e-commerce. Conclusion: It is proved by experiments that the RNN-embed translation model based on the enhanced neural network algorithm can improve the quality of the long sentence translation compared with the machine translation.