Thematic Identification Analysis of Equipment Quality Problems Based on the BERTopic Model

Sining Xu, Yuhui Wang (), Xiangjun Cheng and Qianjun Yang
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Sining Xu: Air Force Engineering University, School of Equipment Management and UAV Engineering
Yuhui Wang: Air Force Engineering University, School of Equipment Management and UAV Engineering
Xiangjun Cheng: Air Force Engineering University, School of Equipment Management and UAV Engineering
Qianjun Yang: Air Force Engineering University, School of Equipment Management and UAV Engineering

A chapter in Proceedings of the 2024 6th Management Science Informatization and Economic Innovation Development Conference (MSIEID 2024), 2025, pp 484-491 from Springer

Abstract: Abstract Aiming at the problem that a large amount of text data on equipment quality problems accumulated in the process of equipment development and production is not effectively used, the paper adopts Bi-directional Long Short Term Memory-Conditional Random Fields model and Bidirectional Encoder Representations from Transformers for Topic Modeling model to identify the topic words of equipment quality problems, and on this basis, combined with clustering method to obtain the topic direction of equipment quality problems. The experimental results show that the proposed model has good performance in the field of equipment quality problems, and it can better obtain the topic words with semantic information, which is better than the current common segmentation and topic recognition methods, and verifies the effectiveness of the model.

Keywords: Quality problems; deep learning; BERTopic; BiLSTM-CRF (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-676-5_47

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