Exploration on the Practice of Translation Teaching for English Majors Based on the Internet of Things and Multimedia Assistance

Abstract

In this paper, an automatic judgment algorithm of English text is proposed, which is split and filtered first, then extracted and optimized, and finally interactively fused, and a BP neural machine judgment system is designed. After machine judgment and teachers' self-judgment of the same English sentence sample, the test results show that the ETSS system has excellent performance, improves the reliability and accuracy of judgment, and reduces the degree of human intervention and misjudgment rate in English translation judgment work. The results of the model translation of the Generalized Maximum Probability Ratio Detection (GLR) algorithm and its recognition include nested data points so that accuracy cannot be effectively guaranteed. In the GLR based comprehensive evaluation of content recognition acquisition improvement, the ID accuracy rate exceeded 95%, and the total result was 92.3%.