Research on Precision-Oriented Training Model for Higher Vocational Smart Logistics Talents Based on Big Data Analysis Smart logistics, powered by IoT, big data, artificial intelligence, and cloud computing, enables full-chain information sharing, process optimization, and predictive decisions across the supply chain. With rapid industrial upgrading and digital transformation, the structure of logistics positions has been shifting significantly, creating a growing demand for versatile professionals with strong digital skills. However, current curricula in higher vocational logistics programs remain traditional and cannot fully adapt to the fast-growing demand for emerging skills. This study integrates multi-source data from recruitment platforms, corporate skill requirements, and industry policies to develop a job demand forecasting model and a course-skill mapping matrix, combined with a personalized learning recommendation mechanism. A closed-loop “Forecast-Match-Feedback-Iterate” framework was implemented. In a pilot program at X Vocational College over one year, job matching rate increased by 28%, employer satisfaction by 22%, and student comprehensive ability index by 0.19, verifying the model’s effectiveness. Key contributions include: introducing a data-driven course updating approach; establishing a skill gap detection system; designing a personalized learning recommendation engine; and developing a practical closed-loop training model. The proposed framework is applicable to smart logistics education and other technology-intensive vocational programs.

Ke Wang. Research on the Precision Training Model for Smart Logistics Talents in Higher Vocational Education Based on Big Data Analysis. International Journal of Educational Innovation and Science (2025), Vol. 6, Issue 1: 154-166. https://doi.org/10.38007/IJEIS.2025.060119.

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