Computable Structures of National Narratives: A Model for Generating Governance Legitimacy Based on Computational Content Analysis, Emotional Mediation, and Semantic Networks

This paper proposes the Computable Structure of National Narrative (CSNN) framework, treating state-level political texts as engineering-oriented governance systems. Using President Xi Jinping's 2026 New Year Address as a case study, it constructs a multi-level variable and causal pathway model encompassing ‘governance input—transformation mechanism—governance output’. The research integrates computational content analysis, sentiment analysis, and semantic network analysis to transform the text into a reproducible variable system: independent variables encompass development/innovation, people's livelihoods, culture, discipline, and external governance narratives; mediating variables include policy perceptibility, emotional resonance, and governance credibility; dependent variables are governance legitimacy and social cohesion; external uncertainty is introduced as a moderating factor. Results reveal: national narratives exhibit stable functional paragraph sequencing; sentiment is not an end-stage effect of communication but a key mediator in generating governance legitimacy; governance legitimacy displays structural output characteristics, dependent on the convergence of multiple mediating pathways. This study contributes a computable, interpretable, and transferable toolchain for political narrative research, providing a reproducible empirical framework for cross-year, cross-national, and multimodal expansion.