Abstract
To address the lack of quantitative cross-industry comparisons in safety risk management and control in power engineering construction, this study develops a cross-industry safety feature-analysis framework that integrates the DeepSeek large language model (LLM) with Python-based automated text mining. The framework is applied to 36 real-world safety cases from 18 enterprises across four major power engineering sectors (hydropower, thermal power, renewable energy, and power transmission and transformation). It uses Python to automatically traverse directories and extract text from paragraphs and tables in documents. A domain-specific lexicon is constructed using prompt engineering with the DeepSeek LLM to generate candidate professional terms. These candidate terms are subsequently verified against the original texts and manually screened before being incorporated into the final lexicon. The validated lexicon is then dynamically loaded into the Jieba tokenizer to improve the recognition of long specialized terms, thereby generating matrix-based word frequency statistics. Both raw and case-normalized word frequencies (CNWF) are calculated to support cross-sector comparison. The results reveal a common characteristic across the examined cases: “dual prevention” institutional support combined with technology-enabled safety management, including grid-based management and video surveillance. However, distinct emphases are observed across subsectors: hydropower cases show greater emphasis on microseismic monitoring and digital twins to mitigate geological risks; thermal power prioritizes institutional checklists and pre-shift safety briefings; renewable energy cases focus on meteorological warnings and remote video surveillance; and the transmission and transformation sector emphasize high-risk construction procedures and comprehensive supervision documentation. These findings indicate that safety management characteristics vary across engineering scenarios and highlight the value of differentiated and scenario-specific safety strategies. Given the relatively small and unevenly distributed sample, the findings should be interpreted as exploratory rather than as statistically representative of the entire power engineering construction industry.