Citation

Shang L, Yin M, Xiao C, Cheng J. China Saf. Sci. J. 2022; 32(6): 103-108.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.06.2262

PMID

unavailable

Abstract

In order to prevent railway safety incidents, text mining related technologies and BLS were utilized to study effective incident classification mechanism, including four categories of equipment, construction, operation and external environmental problems. 314 pieces of text data were cleaned and structured, and Chinese word segmentation was built based on Jieba word segmentation + custom thesaurus+ custom stop word list. Then, 223 feature words were constructed based on Chi square test, and their weights were calculated based on TF-IDF. Finally, accident causes were classified according to BLS, and three classification methods were designed. The results show that the system can form an effective classification model through mining text information of railway safety event reports. And it can save computing power by utilizing features of BLS, and improve classification accuracy by adding feature enhancement nodes, so as improve industry management level. © 2022 China Safety Science Journal. All rights reserved.

Language: zh