[Purpose/significance] The unstructured and highly context-dependent nature of emotional information
poses challenges to traditional intelligence processes. Its deep integration with the emotional labor of practitioners
makes the synergy between "value extraction, labor expenditure, and ethical compliance" a core dilemma. Systematical⁃
ly resolving this dilemma is crucial for enhancing intelligence work efficiency. [Method/process] This study construct⁃
ed an "Emotional Information Intelligence Governance Loop" (EIG-Loop) model that incorporates emotional labor regu⁃
lation, based on intelligence cycle theory. Empirical validation was conducted through a mixed-method study involving
30 open-source intelligence analysts and 400 commercial customer service representatives. [Result/conclusion] The
research identified a four-stage information chain fracture mechanism ("collection-processing-decision-feedback") in
traditional processes and confirmed a significant positive correlation between information entropy and emotional labor
intensity. The EIG-Loop model, via its dual-closed-loop design of "information flow-labor regulation", effectively im⁃
proved intelligence conversion efficiency, reduced emotional labor intensity, and enhanced ethical compliance.