Measuring and Evaluating Digital Talent through Policy Text Analysis

This is the authors' abstract. We don't add key points for this paper.

随着数字经济驱动产业变革，数字人才成为创新发展的核心要素。本文基于政策计量视角，运用内容分析、共词分析及PMC指数模型，对我国中央与地方数字人才政策进行政策文本挖掘与量化评估多维解析，助力构建更为系统、高效、精准的数字人才政策体系，推动数字经济高质量发展，提供坚实的人才支撑与制度保障。本文通过构建涵盖政策目标、工具、主体及实施机制的评估体系，结果发现我国已形成人才培养、引进与激励的全链条政策架构，政策数量与覆盖领域持续扩展，然而部分政策存在目标表述模糊、政策工具组合失衡、部门协同机制缺失等问题。我国数字人才相关政策需进一步优化顶层设计、完善工具组合、强化部门协同等建议，为构建精准高效的数字人才政策体系提供理论支撑与实践路径，助力数字经济高质量发展。With the digital economy driving industrial transformation, digital talent has become a core element of innovation and development. Based on a policy measurement perspective, this study employs content analysis, co-word analysis, and the PMC index model to conduct a multi-dimensional analysis of China’s central and local digital talent policies through policy text mining and quantitative evaluation. The aim is to facilitate the construction of a more systematic, efficient, and targeted digital talent policy system, thereby supporting the high-quality development of the digital economy with robust talent and institutional guarantees. By establishing an evaluation framework encompassing policy objectives, tools, stakeholders, and implementation mechanisms, the findings reveal that China has developed a comprehensive policy architecture covering talent cultivation, recruitment, and incentives, with continuous expansion in policy quantity and coverage. However, certain policies exhibit issues such as ambiguous goal articulation, imbalanced policy tool combinations, and a lack of inter-departmental coordination mechanisms. To address these challenges, this study proposes optimizing top-level design, refining policy tool combinations, and strengthening cross-sector collaboration to establish a more precise and effective digital talent policy system. These recommendations provide both theoretical insights and practical pathways to support the high-quality growth of the digital economy.