Text Mining and PMC-Based Quantitative Scoring of Provincial Data-Asset Policies in China: A Reproducible Evaluation Framework (2019-2025)

Data assetization is essential for enabling market-oriented allocation of data elements and supporting digital-economy development, yet policy design quality varies across regions. This paper proposes a reproducible policy-text evaluation pipeline that integrates ROSTCM6-based text mining with the Policy Modeling Consistency (PMC) index model. A corpus of 178 provincial-level data-asset policies issued by 31 provincial-level administrative regions in China during 2019-2025 is collected from PKULAW via structured retrieval and screening. High-frequency word statistics and semantic network analysis are used to support indicator construction, resulting in a 9-primary/50-secondary evaluation system. Policy quality is quantified through a multi-input-output table, PMC index scoring, grade classification, and PMC surface/radar visualization. Results show an overall mean PMC score of 5.98 ("good" level): policies perform strongly in operability and goal/assessment clarity, while weaknesses concentrate in effectiveness hierarchy, medium- to long-term planning, and policy area coverage. Excellent-grade policies exhibit stronger systematization and broader safeguards, whereas pass-grade policies show narrow coverage and weaker guarantee mechanisms. The proposed framework provides an engineering-oriented method for policy benchmarking and supports targeted optimization of data-asset policy supply.