Refined n-Valued Neutrosophic Markov Decision Processes for Quality Evaluation of Talent Cultivation in Vocational Education under Emerging Productive Forces

Emerging Productive Forces (EPF) including artificial intelligence, green manufacturing, and digital platforms change skill requirements faster than classical curriculum planning can adapt. Standard Markov Decision Processes (MDPs) assume precise transition probabilities and rewards, which under-represent structural indeterminacy in new technologies and fast-evolving job roles. This paper develops a Refined n-Valued Neutrosophic Markov Decision Process (r-nMDP) for optimal vocational curriculum control. In r-nMDP, transition kernels and rewards are expressed as n-valued refined neutrosophic triplets (T,I,F) representing suitability, indeterminacy, and mismatch, respectively. Building on neutrosophic probability and its n-valued refinement, we define a Neutrosophic Bellman Operator, prove its contraction and fixed point uniqueness under a weighted triplet norm, and establish policy improvement in a neutrosophic partial order. We also introduce a Neutrosophic Curriculum Efficiency Index (NCEI) to evaluate and compare policies with explicit penalties on indeterminacy and mismatch. A fully calculated case study with five skill states and three curriculum actions under EPF shocks demonstrates that r-nMDP policies reduce indeterminacy by 20–30% while improving skill–demand alignment by 15–20% compared with a classical MDP baseline. The framework offers a rigorous, uncertainty-aware foundation for designing resilient vocational training strategies in the era of EPF.