Volume 3,Issue 5
Research on Intelligent Competency Modeling and Job-Person Matching Mechanism Based on the DeepSeek Model
With the rapid development of artificial intelligence, especially open-source large language models, enterprise human resource management is undergoing profound transformation. Traditional competency models for job positions largely rely on expert experience for construction, which leads to outdated updates, poor generalizability, and an inability to meet the rapidly changing strategic needs of modern organizations [1]. This paper proposes a low-cost, scalable competency modeling and job-person matching framework based on DeepSeek, a representative open-source language model. By applying natural language processing and semantic embedding techniques, the system extracts competency elements from job descriptions and resumes to construct ability vectors and compute job-person matching scores. The study also explores the application of this model in scenarios such as recruitment screening, job adjustment, and talent development, and identifies challenges such as data quality, model interpretability, and digital literacy among HR professionals, while proposing corresponding countermeasures. Experiments and preliminary applications demonstrate that the intelligent competency system based on large language models is highly feasible and commercially valuable [2].
[1] Gong, M., Fu, Y., Yao, J., et al., 2024, Bibliometric analysis of research on employee competency models in China. Modern
Management, 14(8), 2036–2048.
[2] Maurer R, 2024, Talent Acquisition Trends Led by GenAI, Skills-Based Hiring. SHRM.
[3] Wang J, 2013, Application of competency models in university talent dispatch projects [Master’s thesis, Capital University
of Economics and Business].
[4] Qin, C., Zhang, L., Cheng, Y., Shen, D., Zhu, C., Zhu, H., et al., 2023, A comprehensive survey of artificial intelligence
techniques for talent analytics. arXiv preprint arXiv:2302.12347.
[5] Bohlouli M., Mittas N., Kakarontzas G., et al., 2020, Competence assessment as an expert system for human resource
management: A mathematical approach. arXiv preprint arXiv:2001.00739.
[6] Robert L. P., Pierce C., Morris L., Kim S., & Alahmad R, 2020, Designing fair AI for managing employees in
organizations: A review, critique, and design agenda. arXiv preprint arXiv:2001.00965.
[7] VanderMeulen N., & Leidner D., 2024, Resolving Workforce Skills Gaps with AI-Powered Insights. MIT CISR Research
Briefing, 2024-0401.
[8] AIHR, 2024, HR Competency Model. Amsterdam: AIHR Research Institute.
[9] CPI Knowledge Classroom, 2024, Decoding competency models: A panoramic guide [Zhihu column]. Retrieved June 28,
2025, from https://zhuanlan.zhihu.com/p/671046312
[10] Robert L. P., Pierce C., Morris L., Kim S., & Alahmad R., 2020, Designing fair AI for managing employees in
organizations: A review, critique, and design agenda. arXiv preprint arXiv:2001.00965
[11] Construction of competency models for assistant general practitioners in China based on the Delphi and AHP methods.
General Practice, 2023, 21(2).
[12] Smith A., & Jones R., 2023, Active learning in NLP-based HR systems: overcoming practical implementation hurdles.
Computational Linguistics, 49(4), 209–231.
[13] Johnson H., Patel V., & Kim S., 2021, Explainable AI in HR: deploying XAI tools for transparent decision support. AI
Magazine, 42(2), 67–79.
[14] Chen X., Li Y., & Wang J., 2024, Fairness, transparency, and accountability in AI-driven talent acquisition: A review.
Journal of Business Ethics, 180(1), 45–65.
[15] Allen T., Berg M., & Cummings J., 2022, AI ethics frameworks for enterprise-grade talent management systems. Journal of
Business Ethics, 174(3), 421–437.