Training Mode of Cross-Border E-commerce Japanese Translators Based on Neural Network

Abstract

Deep learning and its application have become a key tool for practical machine learning. Neural networks are equally important to many existing statistical and machine learning methods. In this paper, the author analyzes the training mode of cross-border e-commerce Japanese translators based on neural network. Neural network can also be regarded as a kind of non-linear regression. Based on the theory of system science, the paper reveals the organization structure of Cross-border electric talents training and constructs the talent training system. At the same time, the economic and trade cooperation and cultural exchanges between China and Japan are also increasing. China and Japan are important trading partners of each other. Therefore, Chinese enterprises need a large number of Japanese translators to join in the exchange work. The translation of Japanese trade should be practical. In the fierce competitive market environment, the efficiency of enterprises must be highly efficient. Therefore, translation must be pragmatic.