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Applying data mining techniques for technology prediction in new energy vehicle: a case study in China
1School of Management, Shanghai University, Shanghai, 200444, China.
Summary
This study introduces a novel technology prediction method for new energy vehicles (NEVs) by combining FP-growth and input-output analysis. The method effectively identifies core and emerging NEV technologies for sustained market advantage.
Area of Science:
- Engineering
- Technology Management
- Innovation Studies
Background:
- New energy vehicle (NEV) firms require advanced technology prediction for competitive advantage.
- Identifying core and frontier technologies is crucial amidst fierce industry competition.
- Patent data analysis is a common method for technology prediction.
Purpose of the Study:
- To develop an innovative technology prediction method for NEV firms.
- To identify core and frontier technologies in the NEV sector.
- To analyze technology evolution patterns in China's NEV field.
Main Methods:
- Synthesized the frequent pattern growth (FP-growth) algorithm and input-output analysis.
- Utilized a knowledge flow perspective for technology prediction.
- Analyzed NEV patent family data from 1989-2018 (Derwent patent database).
- Applied multiple co-occurrence analysis to study China's NEV technology landscape.
Main Results:
- Effectively identified core and frontier technologies in the NEV field across different periods.
- Revealed technology layout and evolution patterns within China's NEV sector.
- Demonstrated the proposed method's capability in NEV technology prediction.
Conclusions:
- The developed technology prediction method is effective for NEV firms.
- The method aids in identifying key technologies for market competitiveness and sustainable development.
- Insights into technology evolution support strategic planning in the NEV industry.
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