Article(id=1282030044356788630, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.08.0164, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742745600000, receivedDateStr=2025-03-24, revisedDate=1751126400000, revisedDateStr=2025-06-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589381540, onlineDateStr=2026-07-09, pubDate=1756310400000, pubDateStr=2025-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589381540, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589381540, creator=13701087609, updateTime=1783589381540, updator=13701087609, issue=Issue{id=1282030021623657005, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='8', pageStart='1', pageEnd='276', issueExtLink='null', onlineDate='null', pubDate='1756310400000', pubDateStr='2025-08-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589376121, creator='13701087609', updateTime=1783589731222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031511155228698, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031511155228699, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=48, endPage=53, ext={EN=ArticleExt(id=1282030044566503831, articleId=1282030044356788630, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Analysis of causes of chemical fire accidents based on SIF and social network, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To effectively identify and prevent the risks of chemical fire accidents, a comprehensive research framework was proposed, integrating text mining, SIF model hierarchical analysis, and social network analysis based on rich accident investigation reports in the era of big data. Firstly, the key causes of the accidents were systematically extracted through text preprocessing, term frequency-inverse document frequency (TF-IDF) keyword extraction, and latent dirichlet allocation (LDA) topic modeling, combined with 75 representative chemical fire accident investigation reports from 2000 to 2024. Then, based on the SIF model, the extracted causes were classified into the micro, meso, and macro levels. Subsequently, the accident cause network was constructed using social network analysis methods. Core nodes and key influence paths in the accident cause network were identified through frequency statistics, centrality analysis, and key relationship mining. The research results show that the micro-causes accounted for 56.3%, representing the most crucial factors contributing to chemical fire accidents, with individual factors and environmental equipment risks being dominant. At the meso level, insufficient safety supervision has the highest degree centrality. The key cause path for chemical fire accidents is: inadequate organizational management → insufficient safety supervision → insufficient personal preparation → violation of regulations.

, authors=Pei WANG¹, Xiaoting LI², Rui YANG², Lina ZHENG^{**, 2}, authorsList=Pei WANG, Xiaoting LI, Rui YANG, Lina ZHENG, authorCompany=null, correspAuthors=Lina ZHENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1282030044813967768, articleId=1282030044356788630, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于SIF与社会网络的化工火灾事故致因分析, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为有效识别和防控化工火灾事故风险，遏制化工火灾事故频发，基于数字化技术的深度案例分析，提出一种融合文本挖掘、安全信息流（SIF）模型层次分析与社会网络分析的多维度综合研究框架。首先，对2000—2024年75起化工火灾事故调查报告进行词频-逆文档频率（TF-IDF）关键词提取与潜在狄利克雷分配（LDA）主题建模；然后，基于SIF模型，将提取出的致因按微观、中观和宏观层次进行分类；最后，借助社会网络分析方法构建事故致因网络，通过频数统计、中心性分析及关键关系挖掘，识别出事故致因网络中的核心节点和关键影响路径。结果表明：微观致因占比为56.3%，是导致化工火灾事故的最关键致因因素，其中，个体因素和环境设备风险在化工火灾事故中占据主导地位；中观致因中“安全监督不充分”度数中心度最高，组织管理不到位→安全监督不充分→个人准备不足→违反规章制度是化工火灾关键致因路径。

, authors=王佩讲师¹, 李小婷², 杨睿², 郑丽娜教授^{**, 2}, authorsList=王佩讲师, 李小婷, 杨睿, 郑丽娜教授, authorCompany=null, correspAuthors=郑丽娜教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=HeXcwOka1saMwWQWLNn9AQ==, magXml=hkTY0zQ8PWOynGwB/9EClw==, pdfUrl=null, pdf=/Bw2GxwfaKlU0CK8H3+mJw==, pdfFileSize=6238032, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=31bsbPUkLNPprt4z21RCbg==, mapNumber=null, fund=null)}, authors=[Author(id=1282030045191455136, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030044356788630, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wangpei@cueb.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282030045275341218, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030044356788630, authorId=1282030045191455136, language=EN, stringName=Pei WANG, firstName=Pei, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Management Engineering, Capital University of Economics and Business, Beijing 100070, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282030045338255779, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030044356788630, authorId=1282030045191455136, language=CN, stringName=王佩讲师, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹首都经济贸易大学 管理工程学院，北京 100070, bio={"content":"

王佩（1987—），女，湖北武汉人，博士，讲师，主要从事职业卫生、粉尘治理和人因工效学方面的研究。E-mail: wangpei@cueb.edu.cn。

"}, bioImg=null, bioContent=

王佩（1987—），女，湖北武汉人，博士，讲师，主要从事职业卫生、粉尘治理和人因工效学方面的研究。E-mail: wangpei@cueb.edu.cn。

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