Design of English Translation Model of Intelligent Recognition Based on Deep Learning

Abstract

In the context of globalization, the international common language is English and it is more and more widely used. However, there is a shortage of talents with high English proficiency in all industries. According to the demand of the Chinese market, we should pay attention to students’ English language ability. Deep learning is a machine learning method based on feature learning and feature hierarchical structure, which simulates the analysis and learning of the human brain, carries out feature transformation layer by layer, trains a single-layer network each time, and transforms the features of samples in the original space to a new feature space, thus making classification or prediction easier. There are still some defects in the current machine translation (MT) results of online MT, especially the disadvantages of low efficiency and low accuracy of MT when the full-text range uses the server to compare the data of different languages to obtain the grammar and text-related rules among different languages. Therefore, other modern intelligent recognition technologies should be adopted to achieve accurate English MT. Generally speaking, the closer the training data is to the domain of the target text, the higher the quality of sentence alignment and the more sentence pairs, the more helpful it is to learn more accurate translation rules, so as to obtain a more robust translation.