Research on personalized digital learning resources recommendation model Based on Scenario Tree

Smart education represents the advanced phase of digital education. The intelligent network learning platform must cater to the personalized needs of diverse learners, including students, professional workers, etc., by offering dynamically generated and continuously evolving digital resources. In this paper, taking the electrical automation course as an example, we use data mining techniques to develop a learner feature library, restructures course content and digital resources using knowledge graph, match the learner feature library with learning resources base on scenario tree model to explore personalized learning resource recommendation algorithms, which provides the technical basis for the development of intelligent adaptive learning platform.