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Identifying and Predicting Trends of Disruptive Technologies: An Empirical Study Based on Text Mining and Time Series Forecasting

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  • Minhao Xiang

    (Business School, Ningbo University, Ningbo 315211, China)

  • Dian Fu

    (Teaching and Education College, Ningbo University, Ningbo 315211, China)

  • Kun Lv

    (Business School, Ningbo University, Ningbo 315211, China)

Abstract

Disruptive technologies are related to a country’s competitiveness and international status. Accurately identifying and predicting the trends in disruptive technologies through scientific methods can effectively grasp the dynamics of technological development, adjust the national science and technology strategic layout, and better seize the high ground in international competition. Based on patent text data, this paper uses the improved LDA2Vec model combined with relevant indicators to identify the main topics in disruptive technologies, and predicts and analyzes the development trend through the establishment of an ARIMA model. Taking the energy technology field as an example, the main topics and development trends concerning disruptive technologies in this field are obtained. The study found that ten technologies, including energy storage technology, energy internet management technology, and offshore wind energy technology, are disruptive technologies in the energy technology field, and the development speed of energy storage technology is the fastest. To verify the correctness of the conclusion, this paper compares the results with artificial verification methods such as expert interviews and document verification, and finds that the two are basically consistent, thus verifying the effectiveness and feasibility of the proposed method.

Suggested Citation

  • Minhao Xiang & Dian Fu & Kun Lv, 2023. "Identifying and Predicting Trends of Disruptive Technologies: An Empirical Study Based on Text Mining and Time Series Forecasting," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5412-:d:1101019
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    References listed on IDEAS

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    1. Yunlei Lin & Yuan Zhou, 2023. "Identification of Hydrogen-Energy-Related Emerging Technologies Based on Text Mining," Sustainability, MDPI, vol. 16(1), pages 1-19, December.

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