[目的/意义]情感信息的非结构化与高情境依赖性对传统情报流程构成挑战，其与从业者的情绪劳动强
度深度交织，使得“价值挖掘—劳动损耗—伦理合规”三者间的协同成为核心难题。系统性地解决这一难题，对提
升情报工作效能具有决定性意义。[方法/过程]基于情报循环理论，构建应用于情绪劳动调节的“情感信息情报治
理环路”（EIG-Loop）模型，运用混合研究方法对30名开源情报分析师与400名商业客服两类从业者进行实证检
验。[结果/结论]识别出传统流程中“采集—处理—决策—反馈”四阶信息链断裂机制，并证实信息熵与情绪劳动
强度呈显著正相关。EIG-Loop模型通过“信息流动—劳动调节”双闭环设计，能有效提升情报转化效率、降低情
绪劳动强度，并增强伦理合规性。

[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.