INFORMATION RESEARCH

Approach to Technology Opportunity Prediction Based on Deep Learning: Taking the Case of New Energy Vehicles

  • Gui Meizeng ,
  • Xu Xueguo
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  • 1 Accounting college, Zhejiang University of Finance and Economics, Hangzhou 310018;
    2 School of management, Shanghai University, Shanghai 200444

Received date: 2021-03-30

  Revised date: 2021-07-29

  Online published: 2021-10-09

Abstract

[Purpose/significance] Technology opportunity prediction helps national and enterprise managers to identify the future direction of technology development, to adjust the development strategy and occupy a favorable posture for technology competition.[Method/process] In this paper, a deep learning-based technology opportunity prediction method was proposed. Firstly, the Affinity Propagation (AP) clustering algorithm was applied to achieve the subject classification of technology fields. Secondly, the Doc2Vec algorithm was used to calculate the similarity of patent texts in each technology area, and then identify the technology areas with development potential. Thirdly, the Generative Topographic Mapping (GTM) algorithm was used to map the patent areas with development potential, and we got the technology opportunities through GTM inverse mapping. Finally, a link prediction model based on deep learning was constructed to predict the links of the identified technology opportunities, to obtain the technology opportunities with high development probability.[Result/conclusion] This paper uses new energy vehicle patent data to verify the effectiveness of the method. The results show that the prediction accuracy, recall and F1 value of the deep learning-based link prediction model outperform other prediction models and predict the technology opportunities for new energy vehicles.

Cite this article

Gui Meizeng , Xu Xueguo . Approach to Technology Opportunity Prediction Based on Deep Learning: Taking the Case of New Energy Vehicles[J]. Library and Information Service, 2021 , 65(19) : 130 -141 . DOI: 10.13266/j.issn.0252-3116.2021.19.013

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