人工智能技术对行业创新影响的建模与分析

摘要/Abstract

摘要： 随着人工智能技术的不断发展，各国政府相继推出了资金支持计划和相关政策扶持计划，旨在通过人工智能的创新势头带动各个行业的技术变革。研究人工智能技术的发展对各行业创新的溢出效应具有重要的意义。然而，现有研究主要聚焦定性分析和局部展望，缺乏数据驱动的深入建模和量化分析，难以全面掌握人工智能技术对整个行业创新的影响。本文基于美国30多年的专利数据，构建了专利引用网络，分析了人工智能技术的渗透率和对行业创新的影响。结果表明，人工智能技术对各行业的渗透率呈上升趋势，引用人工智能专利后，行业专利的重要性更加突出。对专利的分层分析表明，仅有头部专利（前20%）推动了行业创新；末尾专利（后50%）的贡献较少，甚至不如未引用人工智能技术的专利。本文的研究结果对于政府制定科学的人工智能扶持政策和引导社会资源的高效配置具有重要的指导意义。

关键词: 人工智能, 专利引用网络, 创新传播, 高阶影响

Abstract: With continuous breakthroughs of Artificial Intelligence (AI) technology, governments worldwide have successively launched funding support plans and related policy support plans to drive technological innovation in various industries through the innovative momentum of AI. Exploring AI technology's development is important for the spillover effects of innovation in various industries. However, the existing work mainly focuses on qualitative analysis and local prospects, lacks data-driven in-depth modeling and quantitative analysis, and it is difficult to fully perceive the impact of AI technology on innovation in the entire industry. This paper analyzes the penetration rate of AI technology and its impact on industry innovation based on more than 30 years of patent data in the United States by constructing a patent citation network. Results show that the penetration rate of AI technology in various industries is continuously rising, and the importance of industry patents is more prominent after citing AI patents. Further layered analysis of these industry patents shows that only a small number of patents (top 20%) promote industry innovation, while the contribution of bottom patents (last 50%) to the value of industry innovation is small, and even less than patents that do not cite AI technology. These results have practical implications for governments to formulate scientific AI support plans and guide efficient allocations of social resources.

Key words: artificial intelligence, patent citation network, innovation diffusion, high-order influence

贾红星, 何赛克, 张培杰. 人工智能技术对行业创新影响的建模与分析[J]. 管理评论, 2026, 38(3): 108-116.

Jia Hongxing, He Saike, Zhang Peijie. Modeling and Analysis of the Impact of Artificial Intelligence on Industry Innovation[J]. Management Review, 2026, 38(3): 108-116.

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