Digital economy has been the new engine for global economic and social development. Digital economy has powerfully stimulated quality reformation, efficiency reformation and power reformation. This paper analyzed the status quo and problem characteristics of the quantitative assessment system for digital economic development in major institutions around the world. A set of quantitative evaluation system and calculation method for the development quality of digital economy was proposed. The evaluation system selects indicators according to the development characteristics of the regional digital economy. The index data was processed using dimensionless processing and weight assignment, and linear interpolation and standard score calculation were used for relative ranking to reduce the subjective influence of the assessment and improve the differentiation characteristics among the assessment objects. The method has been verified by automatic extraction of multiple network open data and achieved good evaluation results.

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An Empirical Study of Automated Measurement Evaluation Based on Network Open Data Collections—The Analysis of the Development Level of Regional Digital Economy

  • Jun Yu

摘要

Digital economy has been the new engine for global economic and social development. Digital economy has powerfully stimulated quality reformation, efficiency reformation and power reformation. This paper analyzed the status quo and problem characteristics of the quantitative assessment system for digital economic development in major institutions around the world. A set of quantitative evaluation system and calculation method for the development quality of digital economy was proposed. The evaluation system selects indicators according to the development characteristics of the regional digital economy. The index data was processed using dimensionless processing and weight assignment, and linear interpolation and standard score calculation were used for relative ranking to reduce the subjective influence of the assessment and improve the differentiation characteristics among the assessment objects. The method has been verified by automatic extraction of multiple network open data and achieved good evaluation results.