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Field identification and opportunity discovery of photovoltaics technology: deep transfer learning method

Author

Listed:
  • Ruilian Han

    (Wuhan University
    Wuhan University)

  • Lu An

    (Wuhan University
    Wuhan University
    Wuhan University)

  • Wei Zhou

    (Wuhan University)

  • Gang Li

    (Wuhan University
    Wuhan University)

Abstract

The advancement of technology relies on scientific and accurate identification of potential opportunities within the field. This study presents a novel method for discovering technology opportunities by combining multi-source data sources and utilizing the word-embedding model, the topic model, and deep transfer learning. The process involves identifying technology fields using sentence vectors that incorporate external semantic knowledge, which addresses the limitations of previous models that only consider word co-occurrence relationships. Real-time Google search data is also integrated to ensure the results are up-to-date. The proposed method was applied to photovoltaics technology and demonstrated impressive performance in enhancing topic coherence and predictive accuracy. The findings indicate that solar cell devices and materials, such as polymer materials and flexible photovoltaic devices, are the most promising technology opportunities based on multi-source data analysis.

Suggested Citation

  • Ruilian Han & Lu An & Wei Zhou & Gang Li, 2025. "Field identification and opportunity discovery of photovoltaics technology: deep transfer learning method," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(8), pages 4283-4307, August.
    • Handle: RePEc:spr:scient:v:130:y:2025:i:8:d:10.1007_s11192-025-05394-z
    DOI: 10.1007/s11192-025-05394-z
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