Analysis of Intelligent Translation Systems and Evaluation Systems for Business English

In order to improve the accuracy of automatic translation of business English, an optimized design of business English translation teaching platform is proposed based on the logistic model combined with deep learning. After using the logistic model to analyze the semantic features of business English translation, the deep learning model is used to segment and mine English images, and the automated lexical feature analysis of business English translation is carried out by using contextual feature matching and adaptive semantic variable finding methods to extract the amount of correlation features between words and vocabulary and to correct the differences in translation in a specific business context to improve the accuracy of English translation. The software design of the platform is carried out under the logistic model, and the platform is mainly divided into a vocabulary database module, an English information processing module, a web interface module, and a human-computer interaction interface module. The test results show that the accuracy of business English translation using this method is good, and the automatic translation capability is strong.