Exploration of the Construction Model of Financial Professional Course System based on Knowledge Map

Yuqin Zhou; Rui Liao; Su Peng

doi:10.54691/zz7npm59

Authors

Yuqin Zhou
Rui Liao
Su Peng

DOI:

https://doi.org/10.54691/zz7npm59

Keywords:

Knowledge Graph; Finance; Continuous Improvement.

Abstract

The current era of social development, industrial transformation, and knowledge renewal has established new requirements for talent cultivation, driving the upgrading and restructuring of education systems. In this context, the development of financial education curricula must closely align with the latest competency demands from specialized sectors like banking, securities, and insurance, enabling dynamic adjustments and refinements. To address this practical need, this study explores a knowledge graph-based continuous improvement model for financial education. Leveraging the inherent advantages of knowledge graph technology, this model accurately identifies core competencies required by the financial industry while intelligently and automatically matching them with existing course content in financial programs. Ultimately, we have established a curriculum optimization system that facilitates bidirectional evaluation and iterative refinement between "industry demand" and "educational outcomes."

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References

[1] P. J. Brindha, B.T. Dai, Y.Fang. A Comprehensive Review of Financial Knowledge Graphs, World Scientific Annual Review of Artificial Intelligence, Vol. 03 (2025), p. 2530001.

[2] X.H. Zou, J.J. Yang, Z.Y. Yang. Research on the Adaptive Transformation of Financial Professional Talent Training in Universities under the Background of Artificial Intelligence, Higher Education and Practice, Vol. 2 (2025), p. 62381.

[3] C.H. Jiang. Research on the Optimization of the Curriculum System for Vocational Undergraduate Business Administration Majors under the Background of "Internet +", New Explorations in Education and Teaching, Vol. 3 (2025), p. 70711.

[4] J.J. Yuan, X.F. Luo, Y. Yin, et al. Optimizing Knowledge Bias: An Adaptive Correction Method for Open Knowledge Graph Construction, Neurocomputing, Vol. 667 (2026), p. 132262.

[5] N. Ebrahimi, M. K. Rezaiyan, M. R. Kasrineh, et al. Longitudinal Educational Achievement and Development Study (LEADS): A Protocol for a Prospective Study Assessing Medical Students' Competencies, BMJ Open, Vol. 15 (2025), p. e098331.

[6] G. Gricourt, T. Duigou, S. Dérozier, et al. Neo4jsbml: Import Systems Biology Markup Language Data Into the Graph Database Neo4j, PeerJ, Vol. 12 (2024), p. e16726.