Research on Intelligent Construction and Dynamic Optimization of Higher Vocational Education Curriculum Systems Based on Transformers and Knowledge Graphs
Main Article Content
Abstract
With the digital transformation of vocational education and the upgrading of industrial demands, the curriculum system of higher vocational education faces core challenges such as lagging industrial alignment, loose knowledge coherence, and insufficient dynamic adjustment. This misalignment is particularly evident in technical fields serving advanced engineering sectors, such as antenna engineering, electromagnetic wave applications, communication equipment manufacturing, and intelligent electronic systems, where graduates are required to master interdisciplinary knowledge and practical engineering skills. To achieve precise alignment between curriculum systems and job requirements, systematic integration of knowledge structures, and dynamic optimization throughout the curriculum lifecycle, this paper proposes an intelligent construction and dynamic optimization method for higher vocational education curricula based on Transformers and knowledge graphs. First, a four-layer knowledge graph linking industry, job, competency, and curriculum is constructed to integrate industrial demands, job standards, and course resources through semantic association. Second, a competency-to-course matching module based on the Transformer model is developed to capture fine-grained correlations between competency requirements and course content through bidirectional attention mechanisms. Third, a multi-objective optimization algorithm is proposed to coordinate the curriculum system’s credit structure, prerequisite relationships, and industrial alignment. Finally, a dynamic adjustment mechanism is established to enable real-time iteration of the curriculum system based on evolving industrial demands and learning effectiveness feedback. This mechanism helps maintain curriculum relevance in rapidly developing engineering fields, especially those related to electromagnetic wave propagation, antenna systems, and communication technology.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
K. Deb, “Multi-objective optimization using evolutionary algorithms,” Chichester: John Wiley & Sons; 2021.
K. Deb and H. Jain, “An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, 2022, doi: 10.1109/TEVC.2013.2281535.
J. Li, L. Chen, and Y. Wang, “Research on the construction of vocational education curriculum system based on industry demand,” Journal of Vocational Education, vol. 44, no. 6, pp. 345-358, 2023.
Y. Zhang, X. Liu, and W. Chen, “Challenges and countermeasures of vocational education curriculum system adaptation to industrial upgrading,” Future Generation Computer Systems, vol. 142, pp. 567-582, 2023.
L. Wang, Y. Zhang, and J. Li, “Fragmentation problems and integration strategies of vocational education knowledge structure,” IEEE Transactions on Education, vol. 65, no. 4, pp. 421-432, 2022.
S. Chen, Y. Liu, and J. Zhang, “Research on the dynamic adjustment mechanism of vocational education curriculum system,” Journal of Educational Technology & Society, vol. 25, no. 3, pp. 123-135, 2022.
Y. Zhang, X. Liu, and W. Chen, “Multi-Scale Convolutional Attention Network for Real-Time Anomaly Detection in Distributed System Logs,” IEEE Transactions on Network and Service Management, vol. 21, no. 2, pp. 1890-1905, 2024.
Zhao Zhiqun, “Development and Trends in Vocational Education Curriculum Development Techniques,” China Vocational and Technical Education, no. 18, pp. 34-40, 2022.
K. Bollacker, C. Evans, and P. Paritosh, “Freebase: A collaboratively created graph database for structuring human knowledge,” Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247-1250, 2008, doi: 10.1145/1376616.1376746.
A. Vaswani, N. Shazeer, and N. Parmar, “Attention is all you need,” Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5998-6008, 2017.
F. Rauner, “The German dual system of vocational education and training,” International Journal of Training and Development, vol. 26, no. 2, pp. 89-105, 2022.
W. N. Grubb and N. Badway, “Community colleges and workforce preparation: What the research tells us,” Educational Evaluation and Policy Analysis, vol. 45, no. 1, pp. 34-52, 2023.
Tsinghua University Research Team, “Research on the Construction of Vocational Education Curriculum Systems Driven by Industrial Demand,” Research on Higher Engineering Education, no. 3, pp. 156-162, 2022.
Zhejiang University Research Team, “Designing Vocational Education Curriculum Structures Based on Job Competency,” Research on Chinese Higher Education, no. 4, pp. 98-104, 2023.
J. Lehmann, R. Isele, and M. Jakob, “DBpedia: A large-scale, multilingual knowledge base extracted from Wikipedia,” Semantic Web, vol. 13, no. 2, pp. 156-179, 2022.
MIT OpenCourseWare, “Knowledge graph-based course planning system,” https://ocw.mit.edu, 2023-09-15.
East China Normal University Research Team, “Construction and Application of Vocational Education Competency Knowledge Graph,” Research in Educational Technology, no. 7, pp. 89-95, 2022.
T. L. Saaty, “The analytic hierarchy process: Planning, priority setting, resource allocation,” New York: McGraw-Hill; 1980.
J. Devlin, M. W. Chang, and K. Lee, “BERT: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
Google Education AI, “Pre-trained transformer models for educational tasks,” https://ai.google/education, 2023-08-20.
NVIDIA A100 GPU technical specifications, “Santa Clara: NVIDIA Corporation; 2020.”
Huawei Cloud Team, “A Transformer-Based Personalized Course Recommendation Method for Vocational Education,” Computer Applications, vol. 43, no. 6, pp. 1890-1896, 2023.
L. L. Spencer, “Competency-based education: Trends, issues, and implications for public policy,” Educational Policy, vol. 36, no. 4, pp. 678-702, 2022.
Huang Yao, “Theoretical and Practical Exploration of Competency-Based Education,” China Vocational and Technical Education, no. 12, pp. 23-29, 2022.
Jiang Dayuan, “Development and Implementation of Competency-Based Curriculum in Vocational Education,” China Vocational and Technical Education, no. 9, pp. 34-41, 2023.
Wu Xueping, “Application and Reflection of Competency-Based Education in Vocational Education,” Research on Education Development, vol. 42, no. 10, pp. 78-84, 2022.
Liu Qiao, Li Yang, and Duan Hong, “A Review of Knowledge Graph Construction Technologies,” Research and Development of Computers, vol. 59, no. S1, pp. 1-18, 2022.
H. Paulheim, “Knowledge graph refinement: A survey of approaches and evaluation methods,” Semantic Web, vol. 14, no. 3, pp. 456-489, 2023, doi: 10.3233/SW-160218.
M. L. Yuan and H. J. Chen, “Research on Multi-source Data Fusion Methods for Knowledge Graphs,” Journal of Computer Science and Technology, vol. 46, no. 5, pp. 1098-1112, 2023.
H. F. Wang, X. Chen, and J. Z. Li, “Research Progress on the Integration of Knowledge Graphs and Deep Learning,” Journal of Computer Science and Technology.