Personalised foreign language learning path recommendation strategy based on disciplinary knowledge graph

With an aim to increase learners' learning efficiency and experience, this paper suggests a personalised foreign language learning path suggestion technique based on subject knowledge graph. Using a knowledge graph in the domain of foreign language studies, integrating multidimensional knowledge points such as vocabulary, grammar, and pragmatics, and revealing their correlations and hierarchical structures, a fusion recommendation algorithm based on multiple learning factors was designed. The method improves learning path recommendation by thoroughly considering the sequence and similarity of knowledge items. According to the trial data, this approach can help students master information points rather successfully, optimise the length of learning paths, and significantly enhance learning interest and enthusiasm, providing important references for the design of foreign language learning systems.