Article Details

Abstract

Against the backdrop of economic transition and the pursuit of high-quality development, long-term regional development is contingent upon strategic adjustments in industrial structure. Taking Shanwei City as a case, this study develops a composite indicator system that integrates economic performance, employment absorption, and ecological constraints. Methods including principal component analysis, time-series forecasting, and machine learning are employed to uncover the characteristics of industrial evolution and to project future trajectories. Building on this foundation, a multi-objective optimization framework is introduced and combined with intelligent algorithms and system dynamics to simulate alternative policy scenarios. The findings indicate that manufacturing upgrading, service-sector expansion, and green transformation follow distinct evolutionary paths, with green transformation showing more pronounced advantages in reconciling economic expansion with environmental improvement. The study provides a quantifiable analytical framework that can serve as a reference for optimizing local industrial structures and informing policy design.

Keywords

industrial structure optimization; Big Data; multi-objective optimization; intelligent algorithm; policy simulation

References

1. [1] RAHUL K，BANYAL R K，ARORA N. A Systematic Review on Big Data Applications and Scope for Industrial Processing and Healthcare Sectors [J/OL].Journal of Big Data，2023，10（1）：133（2023-08-27）[2025-09-11].https://link.springer.com/article/10.1186/s40537-023-00808-2.
2. [2] WANG Z，YE Q L，WANG W L，et al. A Dynamic Task-assisted Constrained Multimodal Multi-objective Optimization Algorithm Based on Reinforcement Learning [J/OL].Swarm and Evolutionary Computation，2025，98：102087[2025-09-11].https://doi.org/10.1016/j.swevo.2025.102087.
3. [3] EVENETT S，JAKUBIK A，MARTÍN F，et al. The Return of Industrial Policy in Data [J/OL].International Monetary Fund Working Paper，2024，2024（1）：31（2024-01-04）[2025-09-12].https://doi.org/10.5089/9798400260964.001.
4. [4] Zheng Lichen, Dai Hongwei, Wang Jianing Optimization of industrial policies, local economic competition, and officials' "rational choices": empirical analysis based on work reports of prefecture level municipal governments [J]. Financial Research. 2024, 50 (5): 108-123
5. [5] China Academy of Information and Communications Technology Research Report on the Development of China's Digital Economy (2023) [R/OL]. (April 27, 2023) [June 27, 2025] http://www.caict.ac.cn/kxyj/qwfb/bps/202304/t20230427_419051.htm.
6. [6] Wu Guanshu, Xu Lei, Han Qing, etc Research on the spatiotemporal evolution characteristics and influencing factors of urban agglomerations based on multi-source big data and panel regression models: taking the coastal urban agglomerations of Guangdong, Fujian, and Zhejiang as an example [J]. Urban Development Research, 2025, 32 (4): 12-17
7. [7] Zhong Zhaohui, Zheng Chengrong, Liang Qiang. Summary of the 2024 China Regional Economics Forum and National Seminar on Regional Economic Discipline Construction [J]. Regional Economic Review, 2024 (6): 157-160
8. [8] Shi Jing, Jiang Xiaoli, Li Jiali Research on the Optimization and Transformation of Regional Industrial Structure [J]. Cooperative Economy and Technology, 2020 (19): 48-49
9. [9] Yu Le'an Research on Theory and Method of Prediction and Decision Optimization Based on Artificial Intelligence [J]. Management Science, 2022, 35 (1): 60-66
10. [10] Li Bo, Quyi The Impact of Industrial Evolution Path Dependence and Breakthrough on Regional Economic Resilience in China's Coastal Regions [J]. Journal of Geography, 2023, 78 (4): 824-839

Funding

Shanwei Vocational and Technical College 2025 Campus level Scientific Research Project (General Project) (2025XJXM007)

About the authors

Zhang Haizhen (1990-), female, Han ethnicity, from Shangqiu, Henan Province, China, teaching assistant, master's degree, research direction: big data technology, computational mathematics, and application of artificial intelligence technology.