随着数字技术深度融入产业活动，高技术产业对数据资源的依赖不断增强，但高技术成果产业化效率仍然有限。基于数字经济与创新经济学理论，结合高技术产业运行与科技管理实践，本文从数据要素配置视角分析产业化过程，构建了“数据要素配置—技术创新—产业化绩效”的分析框架。研究结果表明，合理的数据要素配置有助于降低创新不确定性、缓解信息不对称，并通过优化协同结构提升高技术产业化绩效。在此基础上，提出完善数据治理、强化跨主体数据协同和健全产业化支持机制等政策建议。

With the deep integration of digital technologies into industrial activities, high-tech industries have become increasingly dependent on data resources, while the efficiency of high-tech achievement commercialization remains limited.Based on theories of the digital economy and innovation economics, this study analyzes the industrialization process from the perspective of data factor allocation, drawing on high-tech industry operations and science and technology management practices, and constructs an analytical framework of “data factor allocation–technological innovation–industrialization performance.”The results show that rational data factor allocation can reduce innovation uncertainty, alleviate information asymmetry, and improve high-tech industrialization performance by optimizing collaborative structures. Accordingly, policy recommendations are proposed to improve data governance, strengthen cross-actor data collaboration, and enhance industrialization support mechanisms.