Text Reconstruction Method of College English Textbooks from the Perspective of Language Images

Abstract

With the continuous development of social economy, English learning plays an increasingly important role in daily communication. However, the update speed of English textbooks is far lower than the development speed of English. How to reconstruct the text of English textbooks in colleges and universities to improve the learning effect of college English has become an urgent problem to be solved. In order to solve this problem more effectively, this paper proposes a text reconstruction method for college English textbooks from the perspective of language images. First, this paper proposes a text reconstruction network model for college English textbooks, which includes two self-network models, namely, a text feature extraction network and an image feature extraction network. Second, text designs an English text feature reconstruction network to fuse image features and text features to guide the generation of new English texts according to the generated emotions. Finally, through a large number of experiments, it is proved that the text reconstruction method of college English textbooks from the perspective of language images can effectively generate new texts of college English textbooks, enhance the emotional color of college textbooks, and improve the effect of English learning.