基于深度学习技术的中国能源企业出海英文新闻报道主题建模分析

Topic Modeling Analysis of English News Reports on Chinese Energy Companies Expanding Overseas Based on Deep Learning Technology

摘要: 在全球化和能源转型背景下，中国能源企业加速海外布局，其出海成为国际舆论关注焦点。本研究采用基于深度学习的BERTopic主题建模方法，结合框架理论对185篇国际英文媒体新闻报道进行分析，系统揭示了中国能源企业出海报道的主题分布、地域特征与企业关注态势。研究发现，国际媒体报道涵盖电动车竞争、油气合作、ESG治理等15个主题，呈现传统与新兴议题并存格局；地理分布上，欧美舆论聚焦技术竞争与政策博弈，“一带一路”区域则侧重基建合作；企业层面，国际舆论焦点已从传统的油气企业显著转向新能源企业，反映了中国能源企业在全球角色认知上的转变。本研究为中国能源企业应对国际舆论、优化传播策略提供了实证依据。

Abstract: Amid globalization and energy transition, Chinese energy companies are accelerating their overseas expansion, drawing significant attention from international discourse. This study utilizes BERTopic—a deep learning-based topic modeling method—integrated with framing theory to analyze 185 English-language news reports from international media on the global expansion of Chinese energy firms. It systematically uncovers the topic distribution, geographical patterns, and corporate focus within the coverage. Results identify 15 major topics, including electric vehicle competition, oil and gas cooperation, and ESG governance, reflecting a dual structure of both traditional and emerging issues. Geographically, coverage in Europe and North America emphasizes technology competition and policy challenges, whereas reports related to Belt and Road regions highlight infrastructure collaboration. At the corporate level, media attention has shifted markedly from traditional oil and gas enterprises to new energy players, signaling an evolution in the global perception of China’s role—from resource acquirer to technology and market influencer. These findings provide an empirical basis for Chinese energy companies to shape more adaptive and effective international communication strategies.