The flexible job shop scheduling problem is a complex NP-hard challenge critical to modern manufacturing systems, but existing research predominantly focuses on low-dimensional optimization, practical scenarios often require balancing high-dimensional conflicting objectives such as energy consumption, machine load, tardiness, and completion time. Current -objective algorithms struggle to maintain convergence and diversity in such high-dimensional spaces, leading to suboptimal Pareto fronts. To address this problem, we propose an improved NSGA-III (WDHS-NSGA-III) integrating a reference vector-guided subpopulation partitioning mechanism and a density-aware hybrid search strategy. The algorithm divides populations into subspaces using reference vectors to enable adaptive cross-subpopulation exploration, then applies localized neighborhood searches with five operators to prioritize under-explored regions. Experiments on benchmark instances demonstrate that WDHS-NSGA-III significantly outperforms commonly used methods, achieving balanced optimization across most objectives. The results validate its robustness in resolving high-dimensional tradeoffs, offering a valid solution for smart manufacturing scheduling.