With the growing application of Geographic Information Systems (GIS) in urban management, emergency response, and public services, traditional layer-based thematic map information services—relying on keyword retrieval and layer superposition—can no longer meet users’needs for semantic understanding and intelligent recommendation in cartography. To address this gap, this study proposes a knowledge graph (KG)-driven geographic information recommendation system framework. First, a geographic KG for thematic maps is constructed, focusing on geographic entities and their semantic relationships. High-quality entity relation extraction is achieved using the BERT+BiLSTM+CRF model, while graph embedding representation is implemented via random walk and word2vec to enable high-dimensional matching between user interest vectors and geographic information node vectors. On this basis, a hybrid recommendation model integrating KG semantic reasoning and graph embedding algorithms is designed, and a system prototype with recommendation, visualization, and feedback optimization capabilities is developed. Experimental results demonstrate that the system outperforms traditional methods in both recommendation accuracy(Precision@10: 0.78 vs. 0.58–0.67 for traditional methods) and processing speed(average response time: 250 ms vs. 370–410 ms for traditional methods), with strong practicality and scalability. This research achieves innovative breakthroughs in knowledge extraction, hybrid recommendation strategies, and system performance optimization, providing effective support for semantic scenario-oriented geographic information services.
