Against the backdrop of intensifying global artificial intelligence (AI) competition, frequent fluctuations in national rankings across evaluation indices not only reflect shifts in national capabilities but also reveal how index design logic influences and shapes assessment outcomes. To interrogate this phenomenon, this paper first selected nine mainstream AI evaluation indices as research samples based on five principles: representativeness, professionalism, relevance, transparency, and timeliness. Using the TF-IDF algorithm for taxonomic categorization of original indicators, five thematic dimensions were identified: Data Infrastructure, Talent Pool, R&D Capacity, Investment & Deployment, and Strategy & Governance. Employing this framework, we conducted cross-sectional comparisons of scores across these dimensions for 15 leading nations and longitudinal tracking of their rank variations across indices and time periods. Key findings indicate that: Ranking volatility stems primarily from adjustments in indicator weighting schemas and scoring granularity, with indices embedding distinct technological priorities and value orientations; The Strategy & Governance dimension exhibits high reactivity to policy signals, while other dimensions demonstrate greater temporal stability. This research proposes a standardized five-dimensional evaluation architecture incorporating dynamic weight calibration and an annual rolling revision mechanism. This approach achieves dynamic equilibrium between international comparability and contextual adaptation, providing methodological scaffolding for constructing versatile yet responsive AI assessment frameworks.