With the rapid development of smart logistics technologies, the industry’s demand for multi-skilled, data-driven talents is increasing. Addressing the current issues in vocational colleges—such as inconsistent competency evaluation standards and mismatched curricula with industry requirements—this study constructs a systematic competency indicator framework for smart logistics talents in higher vocational education. A total of 11,500 valid job postings were collected from online recruitment platforms, enterprise surveys and expert interviews. Text mining and cluster analysis were applied to identify five core competency dimensions: technical skills, data analytics, process management, collaborative communication, and comprehensive literacy. Furthermore, the Analytic Hierarchy Process (AHP) combined with the Entropy Method was utilized to assign weights to each dimension and sub-indicator, resulting in a three-tier quantitative framework. An empirical verification was conducted on students majoring in smart logistics at a selected vocational college, using dual-source evaluation from corporate HR and teachers to test its validity and applicability. The results proved that the indicator system can be effectively applied to curriculum development, student competency assessment, and recruitment evaluation, providing a feasible pathway for integrating education with industry in the smart logistics domain. This study not only bridges the gap in standardized competency assessment in this field but also offers references for other emerging technology areas in vocational education.

Ke Wang. Research on the Precision Competency Indicator System for Smart Logistics Talents in Higher Vocational Education Based on Big Data Analysis. International Journal of Educational Curriculum Management and Research (2025), Vol. 6, Issue 1: 188-199. https://doi.org/10.38007/IJECMR.2025.060122.

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