The Construction of Higher Vocational English Language Bank Based on Neurolinguistic Knowledge

With the advent of artificial intelligence and big data technologies, traditional approaches to teaching English in higher vocational education are increasingly inadequate for contemporary pedagogical demands. This study involved the collection and analysis of an extensive corpus of English learning materials facilitated by the use of neural network models to process this data. Consequently, we established an English language library tailored to the authentic language use and learning needs of higher vocational students. A substantial array of textual materials pertinent to higher vocational English instruction, including textbooks, online articles, and practical dialogues, was amassed using web crawler technology. These texts underwent natural language processing that included tokenization and the elimination of stop words. To develop a language model, we deployed a Transformer-based neural network for deep learning analysis of the processed data. Our findings indicate that the constructed language bank excels over traditional higher vocational English teaching resources in terms of vocabulary breadth, grammatical diversity, and practical utility. The language bank demonstrates notable benefits in analyzing vocabulary usage biases and enhancing students’ pragmatic language skills. This research substantiates the feasibility and effectiveness of constructing a language bank for higher vocational English utilizing neurolinguistic methodologies. The integration of advanced language modeling and big data processing technologies can significantly elevate the caliber of educational resources and instructional efficacy