Knowledge graph-based adaptive recommendation model for training courses

The contradiction between course resource overload and learners' personalised needs in online education platforms is becoming increasingly prominent.Addressing the common issues of weak interpretability and poor dynamic adaptability in existing recommendation methods, this paper proposes a knowledge graph-based adaptive course recommendation model.By constructing a hierarchical knowledge graph to precisely represent the course knowledge system and integrating deep knowledge tracking with reinforcement learning techniques, the model dynamically perceives learners' knowledge states and evolving interests, enabling real-time adjustment of recommendation paths.Experiments on the publicly available china university massive open online course dataset demonstrate that compared to mainstream baseline models, our model achieves up to 8.7% higher performance on key metrics such as normalised discounted cumulative gain@10 and hit rate @10.This validates its effectiveness and superiority in delivering precise, explainable personalised recommendations.