Empirical evaluation reveals superior practical engagement in integrated training models among rural trainees, indicating the critical role of hands-on work duration in talent development.
The cultivation of local talent skilled in advanced techniques serves as a key support for rural revitalization, while also facilitating digital information exchange and intelligent industrial coordination in modern networked environments. As emerging communication infrastructures and data-driven technologies continue to enhance resource connectivity, traditional training models still face the persistent challenge of “theory-practice disconnect,” where classroom knowledge cannot be effectively translated into practical productivity. To address this issue, this study proposes a datadriven “immersive” collaborative cultivation framework for rural local talent based on representative practices including Shandong Shen County’s “store-front, school-back” model, Hubei Luotian County’s “1+N” teaching approach, and Guangxi Quanzhou County’s “base + cloud + field” system. An evaluation index system containing four first-level indicators, twelve second-level indicators, and thirty-six observation points is established, together with an AHP-entropy combined weighting method and a fuzzy matter-element comprehensive evaluation model. The participation process is redefined by emphasizing task-based mastery rather than conventional attendance-oriented metrics. Empirical results demonstrate that Shen County significantly outperforms the other two regions in practical participation and market linkage, while instrumental variable regression confirms that on-job duration has a causal effect on cultivation effectiveness beyond trainee self-selection. The findings suggest that rural talent development should shift from process -oriented to results-oriented logic by prioritizing practical training, continuous follow-up mechanisms, and differentiated cultivation strategies. The proposed framework provides an operational solution for evaluating talent cultivation and offers useful insights for intelligent information-driven management and collaborative digital infrastructures that support advanced communication and electromagnetic-enabled industrial systems.
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Chen et al. (2026) studied this question.
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