全球人工智能评价体系存在“测量黑箱”与“战略误判”挑战。为系统性地解构其内在结构，文章整合了22个主流人工智能评价指数（637项原始指标），构建了包含307项可比指标的标准化库，并采用双维度分析（注意力分布与复杂网络建模）揭示其内在运行逻辑。研究首次识别并命名了该领域主导的国家能力本位的规模化增长评价范式。该范式以国家能力为评价核心，由“政策-技术”双引擎驱动，在衡量标准上存在显著的规模导向偏好，并因此在创新质量、成果转化等维度存在结构性盲区。文章通过提供这一范式的结构蓝图，为规避战略误判、设计更均衡的下一代评价框架提供了关键的实证路径。

The global artificial intelligence (AI) evaluation system faces challenges of "measurement black boxes" and "strategic misjudgments." To systematically deconstruct its internal structure, this study integrates 22 mainstream AI evaluation indexes (637 original indicators), constructs a standardized library of 307 comparable indicators, and uses a dual-dimensional analysis (attention distribution and complex network modeling) to reveal its internal operational logic. The study, for the first time, identifies and names the dominant "national capacity-based, scale-oriented growth" evaluation paradigm in the field. This paradigm takes national capacity as its evaluation core, is driven by a "policy-technology" dual engine, exhibits a significant scale-oriented bias in its measurement standards, and consequently has structural blind spots in dimensions such as innovation quality and conversion efficiency. By providing a structural blueprint of this paradigm, this study offers a critical empirical pathway for avoiding strategic misjudgments and designing more balanced next-generation evaluation frameworks.