| 何文珍,苑朋彬.中国中央及地方政府数据治理政策主题演化比较分析[J].全球科技经济瞭望,2025,40(10):56~67 |
| 中国中央及地方政府数据治理政策主题演化比较分析 |
| Comparative Analysis of the Evolution of Data Governance Policy Themes of Central and Local Governments in China |
| 投稿时间：2025-07-06 |
| DOI：10.3772/j.issn.1009-8623.2025.10.007 |
| 中文关键词: 数据治理；政策文本；主题分析；LDA；主题演化分析 |
| 英文关键词: data governance; policy texts; thematic analysis; LDA; thematic evolution analysis |
| 基金项目:2025 年浙江省教育厅一般科研项目“生成式 AI 赋能高校图书馆知识产权信息服务模式研究”（Y202557350）；2024 年度 教育部产学研合作育人项目“智慧图书馆数据治理体系研究”（241204171172034）；2024 年绍兴文理学院校级科研项目“数 字人文背景下高校图书馆助力地域文化建设路径研究”（2024SK007）。 |
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| 摘要点击次数: 6 |
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| 中文摘要: |
| 以中央及地方政府数据治理政策文本为数据源，运用隐含狄利克雷分布（Latent Dirichlet
Allocation，LDA）主题模型，结合时间、空间等维度对政策主题内容及演化趋势进行比较分析。研
究发现，中国数据治理政策主题演化整体呈现“基础管理→融合协同→价值赋能”的演进规律；中央
层面的政策具有显著的战略引领特征，聚焦数字经济发展、数据要素市场化配置等国家战略，注重跨领
域协同与国际竞争力提升，而地方政府的政策则紧密结合区域特色进行发展定位。基于政策文本挖掘
结果，从建立区域间政策经验共享机制、构建区域协同政策体系、突出地区特色和平衡数据治理各环节
投入等方面，为完善中国数据治理政策体系提出建议与对策。 |
| 英文摘要: |
| Thaking the policy texts on data governance from central and local governments in China as the data
source, this study employs the Latent Dirichlet Allocation (LDA) topic model, combined with dimensions such
as time and space, to conduct a comparative analysis of policy topics and their evolutionary trends. The findings
reveal an overall evolution pattern in Chinese data governance policies: “Foundational Management → Integration
& Collaboration → Value Empowerment”. Central government policies exhibit significant strategic leadership
characteristics, focusing on national strategies like digital economic development and market-oriented allocation of
data elements, emphasizing cross-domain collaboration and enhancing international competitiveness, emphasizing
cross-domain collaboration and enhancing international competitiveness. In contrast, local government policies
are closely integrated with regional characteristics for development positioning. Based on the results of policy
text-mining, this paper proposes suggestions and countermeasures to improve China’s data governance policy
system, including establishing mechanisms for sharing policy experiences across regions, constructing coordinated
regional policy systems, highlighting regional distinctiveness, and balancing investments across different aspects
of data governance. |
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