This invention discloses a method,
system, and device for recommending learning paths in online communities based on knowledge graphs. First, a
knowledge graph model is constructed, consisting of three
layers of entities: "course – topic – knowledge point." A multi-level text similarity function based on a corpus and core phrases is then constructed using user posts in the
online community. Second, horizontally, the similarity function measures the
semantic similarity between posts. Based on the user's knowledge background, a
unique user with a similar
knowledge level within the
user group is identified, and their learning path is obtained. If a path is missing, vertically, the
knowledge graph is used to recommend users at a higher level than the current topic's knowledge point. A
unique user is identified based on their knowledge background, and their posts are obtained. Finally, the
knowledge graph is used to recommend fine-grained and multi-context-aware learning paths for users in the
online community. This invention can recommend fine-grained and multi-context-aware learning paths from both
horizontal and vertical perspectives, combined with the user's knowledge background.