Studying the role of English and American literature in the dissemination of digital health concepts

Abstract

Abstract This paper first analyzes the implementation of digital health education in English and American literature descriptively, based on the principles and characteristics of language art in English and American literature. Then, based on the word vector model, the CBOW model and Glove model are proposed for assigning weights to words with different occurrence frequencies, followed by linear splicing of word vectors and their additional feature vectors in sentences into final word vectors fed into the semantic analysis model, thus eliminating the phenomenon of illegal labels being predicted, improving the accuracy of semantic analysis prediction, and empirically analyzing English and American literary works under the concept of digital health analysis. The results showed that on digital health, the mean value of students’ evaluation of their mental health status in class 4 increased from 2.88 in the pre-test to 3.48 in the post-test, and the overall evaluation scores of mental health status were on an upward trend. For the model performance analysis, AvgPool’s summation and averaging of all features in the pooling domain can retain more linguistic information and reduce the training parameters to improve the model performance further. This study explores the concept of digital health in English and American literature to improve one’s cultural literacy.