Classification and Informatization of Subject Resources for English Language Teaching in the Context of Data Theory

Abstract

Abstract The construction of information resources based on the development needs of disciplines is the fundamental guarantee for carrying out disciplinary service work. An English teaching resource guarantee system is created by combining the OAI protocol with Open URL technology in this paper. Furthermore, the VIPS algorithm is used for style tree construction, combined with split bar weights and mixed text density for extracting web page body information. Then, the weight factor is introduced to improve the PageRank algorithm, and the critical resource selection strategy is combined to achieve the mining and classification of English teaching resources. In addition, the study introduces a knowledge graph for entity recognition and combines graph convolutional networks with user preference weights for optimizing teaching resource recommendations. Based on this basis, the performance of the constructed English teaching resources assurance system is verified for webpage information extraction, teaching resources classification, and personalized recommendations. It is found that the correct rate of English webpage information extraction is 96.42% when the text density of the preceding and following text is 0.334 and 0.527, respectively, and the personalized recommendation performance of English teaching resources is 4.12% higher than the best-performing KGCN model in terms of AUC index. The English teaching resources guarantee system can effectively ensure the precise classification of teaching resources in the English language and provide new guidance for furthering the automation of English teaching resources.