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2023 , Vol. 67 >Issue 12: 89 - 102

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.12.009

INFORMATION RESEARCH

Identification of Disruptive Technology Topics Based on LDA2Vec and DTM Models: A Case Study in the Energy Technology Field

  • Lü Kun ,
  • Xiang Minhao ,
  • Jing Jipeng
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  • 1 Business School, Ningbo University, Ningbo 315211;
    2 School of Business and Management, Jilin University, Changchun 130022

Received date: 2022-12-26

  Revised date: 2023-03-22

  Online published: 2023-07-06

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Abstract

[Purpose/Significance] Disruptive technology is crucial to a country’s competitiveness and international status. Accurately identifying the topics of disruptive technology can effectively address the problems of unclear themes and development paths in the process of technology development. This can provide a better grasp of the dynamics of technology development, adjust the layout of national science and technology strategies, and better seize the international competitive high ground. [Method/Process] This study used patent text data in the field of energy technology as the research object, constructed a fusion feature vector based on Word2Vec word vectors and LDA topic vectors, and introduced the K-means algorithm to optimize the topic clustering effect. Finally, combining with the characteristic indicators of disruptive technology, the disruptive technology topics were identified, and the DTM model was used to reveal the development status of disruptive technology topics in this field. [Result/Conclusion] Through manual verification and comparison with model results, the empirical results are reasonable. The precision, recall, and F1 values of the model are higher than those of similar topic models, which proves that this method is effective in identifying disruptive technology topics.

Key words： disruptive technology; topic identification; text mining; energy technology field

Cite this article

Lü Kun , Xiang Minhao , Jing Jipeng . Identification of Disruptive Technology Topics Based on LDA2Vec and DTM Models: A Case Study in the Energy Technology Field[J]. Library and Information Service, 2023 , 67(12) : 89 -102 . DOI: 10.13266/j.issn.0252-3116.2023.12.009

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