B research on the construction method of knowledge graph based on NLP under the "double reduction" Policy
Ruijun Xu · IET conference proceedings. · 2025
With the implementation of China's "double reduction" policy, secondary education has entered a new stage, requiring more efficient knowledge transfer and subject expansion. This paper studies the secondary school knowledge graph based on natural semantics, aiming to improve the integration of subjects by deeply mining the semantic association of knowledge. The study first analyzes the impact of the "double reduction" policy on education, emphasizing the importance of streamlining and integrating the knowledge system. Using deep learning and natural language processing technology, combined with textbooks and syllabus data, a multi-dimensional secondary school knowledge graph was constructed. By comparing the traditional knowledge system and the natural semantic knowledge graph, the advantages of the natural semantic method in knowledge relevance and deep expansion were verified. The results show that this knowledge graph can express knowledge points more intuitively and provide a reference for innovation in secondary education. Finally, the limitations of the study are discussed and future directions are prospected.