Text analysis of power customer complaint work order based on data mining

DaoJing Huang; WenTing Zhu; Qian Wang; Fei Shao; Jing Zhao

doi:10.1117/12.2681137

23 May 2023 Text analysis of power customer complaint work order based on data mining

DaoJing Huang, WenTing Zhu, Qian Wang, Fei Shao, Jing Zhao

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126453Q (2023) https://doi.org/10.1117/12.2681137
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

In order to explore the problem of text analysis of power customer complaint work order, a text analysis of power customer complaint work order based on data mining is proposed. This method explores the research on the text analysis of power customer complaints work order through the key technical problems and solutions of information recommendation represented by data mining. The research shows that the combined curve is closer to the actual situation, and all prediction indicators have improved, indicating that the combination effect is good. At present, the data platform is still in the process of improvement. Therefore, enterprises cannot be complacent and stay at the current analysis stage. They should keep pace with the times, strive to improve the big data platform, and further improve their data mining and analysis capabilities to adapt to the increasingly fierce market competition.

Citation Download Citation

DaoJing Huang, WenTing Zhu, Qian Wang, Fei Shao, and Jing Zhao "Text analysis of power customer complaint work order based on data mining", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126453Q (23 May 2023); https://doi.org/10.1117/12.2681137

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