Applying data mining techniques for technology prediction in new energy vehicle: a case study in China

Technology prediction is an important technique to help new energy vehicle (NEV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative technology prediction method to effectively identify core and frontier technologies for NEV firms. Among the various methods of technology prediction, one of the most frequently used methods is to make technology prediction from patent data. This paper synthesizes the frequent pattern growth (FP-growth) algorithm and input-output analysis to construct a new technology prediction method based on the knowledge flow perspective, takes the data of NEV patent family in 1989-2018 the Derwent patent database as a sample, divides the data according to the 5-year standard, and uses the method to identify the core and frontier technologies in the NEV field during different periods. Furthermore, the multiple co-occurrence method applies to analyze the technology layout and evolution patterns in China's NEV field. The results show that the technology prediction method proposed in this paper can effectively identify core and frontier technologies to achieve NEV technology prediction.

Automated summary

This study combines the FP-growth algorithm and input-output analysis to propose a new technology prediction method based on the knowledge flow perspective, which effectively identifies core and frontier technologies in the NEV field during different periods. The method uses NEV patent data samples and applies the multiple co-occurrence method to analyze technology layout and evolution patterns in China's NEV field, demonstrating its effectiveness in NEV technology prediction.