Research on Content Organisation and Ecological Optimisation Strategies for English Learning in Colleges and Universities Based on Knowledge Graphs

After years of developing English education in colleges and universities, its ecology is urgently in need of optimization and innovation in today’s rapidly changing environment. This paper introduces the knowledge graph into the study of English learning content in colleges and universities, collects the data on English learning content in colleges and universities, and extracts the effective data from the complicated related information data after constructing the ontology of the knowledge diagram manually. Then, a graphical database is used to store them. Create the entity-relationship joint extraction model, the head entity recognition module, the relationship module, and the tail entity recognition module. Then, correctly extract the entity relationships to build the knowledge graph for college and university English learning content. Conduct similarity matrix analysis and cluster analysis on high-frequency keywords in English learning content at colleges and universities and propose corresponding optimization suggestions. Among the high-frequency keywords in the English learning content of colleges and universities, vocabulary has the highest frequency of occurrence, which is 86 times. The top 6 keywords with the highest frequency are: vocabulary, CET-4, comprehension, CET-6, vocabulary, and expression, in order. The keywords are ranked in descending order of closeness distance: grammar (0.326) > CET-4 (0.285) > CET-6 (0.217) > comprehension (0.134) > expression (0.096). The content of English learning in colleges and universities can be clustered into 3 categories (English application skills, English fundamentals, and specific content).