Against the backdrop of the Fifteenth Five-Year Plan, the deep integration of artificial intelligence (AI) with traditional industries has become a major pathway for manufacturing upgrading. Zhejiang is both a major manufacturing province and a major textile province in China, featuring a relatively complete industrial chain and rich application scenarios ranging from R&D and design to production, supply chain coordination, and brand marketing. Drawing on publicly available policy documents, industrial reports, and representative cases, this paper examines the major application scenarios of AI in Zhejiang’s traditional textile industry, identifies the key bottlenecks constraining intelligent transformation, and proposes a policy-oriented framework for industrial upgrading. The study finds that AI is already penetrating four core domains: design and product development, smart production and quality control, supply chain coordination, and data-driven marketing. However, further diffusion remains constrained by weak data foundations, unclear scenario prioritization, mismatches between technology supply and industrial demand, high transformation costs, shortages of cross-disciplinary talent, and insufficient public service support. Based on domestic and international experience, the paper argues that the next stage of Zhejiang’s textile upgrading should move beyond general digitalization and focus instead on scenario-based deployment, chain-level coordination, platform support, standardization, and talent development. The practical value of AI lies not merely in isolated efficiency gains, but in reconfiguring the logic of design, production, coordination, and market response across the textile value chain.
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Share and Cite
Zhu, J. (2025) Artificial Intelligence Empowering the Renewal and Upgrading of Zhejiang’s Traditional Textile Industry: Application Scenarios, Bottlenecks, and Policy Responses. Scientific Research Bulletin, 2(6), 91-95.