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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Construction of Vocational Education Knowledge Graph Based on DeepSeek
WANG Xiaoyu
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DOI:10.17265/2161-623X/2025.09.004
Guangzhou Civil Aviation College, Guangzhou, China
In the context of the digital and intelligent era, exploring effective paths for the digital transformation of vocational education through artificial intelligence technology is of vital importance. This research takes the “Compressor” chapter of the “Civil Aircraft Maintenance Personnel License Management Rules” (CCAR-66-R3) license course “Gas Turbine Engine” as the object and proposes an automatic construction method for vocational education course knowledge graphs based on the DeepSeek large language model (LLM). By designing targeted prompt words (Prompts), the model API is invoked to extract (entity-relation-entity) triples from unstructured textbook texts, and Neo4j graph database is used for storage and visualization. Eventually, a structured knowledge network representing the structure, working principle, and fault diagnosis of the compressor system is formed. This method verifies the feasibility of large language models in the structured processing of complex technical field knowledge and provides a reference technical solution for the efficient construction of knowledge bases on the vocational education field and the support of intelligent teaching applications.
knowledge graph, large language model, vocational education, triple extraction
WANG Xiaoyu. (2025). Construction of Vocational Education Knowledge Graph Based on DeepSeek. US-China Education Review A, September 2025, Vol. 15, No. 9, 631-638.
Cao, R. Y., & Cao, S. J. (2023). The effect and enlightenment of ChatGPT to complete the task of knowledge organization. Information and Documentation Services, 44(5), 18-27.
Dai, G. H., Wu, X. G., & Zhan, W. H. (2025). Research on the solution and development of DeepSeek local deployment in terminals. Mobile Communications, 49(3), 100-106.
Deng, Z. J. (2022). AI-based knowledge graph construction technology and its application. Radio Engineering, 52(5), 766-774.
Gao, C. J., & Mu, S. (2024). Virtual simulation teaching strategy to promote deep learning of vocational skills—Taking “aircraft maintenance professional teaching” as an example. Laboratory Research and Exploration, 43(12), 165-171.
Hang, T.-T., Feng, J., & Lu, J.-M. (2021). Knowledge graph construction techniques: Taxonomy, survey and future directions. Computer Science, 48(2), 175-189.
Hu, T. (2022). Design and implementation of automatic compilation of maintenance work card based on Python. Aviation Maintenance & Engineering, 67(6), 59-62.
Huang, H., Yuan, S., He, T.-T., & Wu, L. J. (2019). Research on the construction of course knowledge graph for adaptive learning system—Taking “Java Programming Foundation” course as an example. Modern Educational Technology, 29(12), 89-95.
Li, Z., & Zhou, D. D. (2019). Research on conceptual model and construction method of educational knowledge graph. e-Education Research, 40(8), 78-86+113.
Liu, C., Zhou, K., & Li, J. L. (2025). Development and practice of new form textbooks for aircraft maintenance specialty under the background of integration of production and education. Guangdong Vocational Technical Education and Research, 16(4), 96-99+143.
Liu, S. N. Y., & Hao, X. H. (2024). The challenges and approaches of generative artificial intelligence in promoting educational innovation. Tsinghua Journal of Education, 45(3), 1-12.
Wang, M., Tang, X. L., & He, T. T. (2014). Research on a multi-document summarization method based on text segmentation technology. Computer Applications and Software, 31(9), 40-44.
Xiao, M. S., Wang, M., Guo, Y. Q., & Luo, J. M. (2022). Research on the construction of knowledge graph of programming language course. Journal of Gannan Normal University, 43(6), 95-100.
Xie, J., Yang, H. Y., Liang, F. M., & Xu, X. Y. (2025). Design and development on intelligent question & answering system based on the course graph—Taking signals and systems as an example. Chinese Journal of Systems Science, 33(3), 161-167.
Yang, Y., & Rai, S.-N. (2025). Research on knowledge graph based on collaborative knowledge construction conversation. Modern Educational Technology, 35(8), 97-106.
Zhang, H. N. (2023). Construction and application of curriculum knowledge graph for intelligent education (Chinese Master’s theses full-text database, 2023).
Zhang, X. S., Liu, L., Wang, H. L., Su, G. B., & Liu, J. (2024). Survey of entity relationship extraction methods in knowledge graphs. Journal of Frontiers of Computer Science and Technology, 18(3), 574-596.