Design of a Constructive English Translation Intelligent Recognition Model Based on Deep Learning and RBF Algorithm - Kang, Yi (2023 International Conference on Network, Multimedia and Information Technology (NMITCON), 2023)
In today's globalized society, English has become one of the most widely used communication languages, and the accuracy of English translation is crucial for cross language communication. Therefore, this experiment aims to explore the application of deep learning and RBF (Radial Basis Function) algorithm in the design of English Translation Intelligent (ETI) recognition models. Through experiments, it was found that the combination of deep learning and RBF algorithm could improve the translation accuracy and sentence similarity of English translation models. Deep learning and RBF algorithms could improve the model's translation accuracy and sentence similarity, respectively. Combining them could significantly improve the model's translation performance. Specifically, the experimental results showed that the translation model combining deep learning and RBF algorithm achieved a translation accuracy of 90% and sentence similarity of 80%. The translation accuracy of using deep learning models alone was 75%, and sentence similarity was 70%; the translation accuracy using RBF algorithm alone was 80%, and the sentence similarity was 75%. This indicated that deep learning and RBF algorithms could be applied to the design and implementation of intelligent recognition models for English translation. The combination of the two could improve translation accuracy and sentence similarity.