全球人工智能评价指标体系结构解构与评价范式研究

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中图分类号：TP18 文献标识码：A DOI：10.11968/tsyqb.1003-6938.2025042

Abstract The global artificial intelligence （AI） evaluation system faces chalenges of "measurement black boxes"and "strategic misjudgments."Tosystematicallydeconstructitsinteralstructure，thisstudyintegrates 22 mainstreamIevaluationindexes （637original indicators），constructsastandardized libraryof3O7comparable indicators，andusesadualdimensionalanalysis （atention distributionandcomplex networkmodeling）toreveal its internaloperationallogic.The study，forthefirsttime，identifiesandnamesthedominant"nationalcapacityased，scaleorientedgrowth"evaluation paradigminthe field.This paradigmtakesnationalcapacityasits evaluationcore，is drivenbya"policy-technology"dual engine，exhibitsasignificant scale-oriented bias initsmeasurement standards，andconsequentlyhasstructural blind spots in dimensionssuchas innovation qualityand conversion efficiency.Byproviding astructural blueprintofthis paradigm，this study offers acritical empirical pathway foravoiding strategic misjudgments and designing more balanced next-generationevaluation frameworks.

Key words artificial inteligence evaluation; evaluation indicator system; complex network analysis; evaluation paradigm; structural deconstruction

在全球新一轮科技革命与产业变革的浪潮中，人工智能（AI）已成为大国战略博弈的核心领域。（剩余12603字）