Analysis of key knowledge in English teaching based on knowledge graph model

In the context of digital education, this study proposes a data-mining–driven approach to improve key knowledge analysis in English teaching. A small-sample relational inductive reasoning model is developed by integrating graph neural networks (GNNs) with meta-learning to address data scarcity and personalised learning demands. An English teaching knowledge graph is constructed using digital textbooks, lesson plans, and authoritative language knowledge bases, with entity and relation extraction techniques used to model semantic relationships. To enhance inductive reasoning under limited data conditions, path masking and loss-weight meta-learning strategies are introduced. Experimental results show that the proposed model imports tens of millions of nodes in 38 seconds, achieving an 82% efficiency improvement over the basic GNN model. The multi-hop query delay is reduced to 6 ms, 98% lower than that of traditional databases, with significantly optimised memory usage. In practical teaching applications, students’ average reading scores increase by 8.2 points, grammar mastery reaches 92%, personalised learning path matching achieves 88%, and the repetition rate of incorrect answers decreases by 72%. Overall, the proposed method substantially enhances English teaching effectiveness and learning outcomes.