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Identification of Key and Core Technologies Based on Patent Competitiveness Index and Doc-LDA Topic Model : A Case Study of New Energy Vehicles
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Teng Fei,Zhang Qi,Qu Jiansheng,Li Haiying,Liu Jiangfeng,Liu Boyu
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(School of Economics and Management, China University of Petroleum-Beijing, Beijing, 102249, China)
(National Science Library, Chinese Academy of Sciences, Beijing, 100190, China)
(School of Business Administration, China University of Petroleum (Beijing) at Karamay, Xinjiang, 834000, China)
(Tianshan Research Institute, Xinjiang, 834000, China)
(Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu, 610041, China)
(School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China)
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Abstract
[Purpose] Accurately identifying key and core technologies is of great significance for understanding critical technology domains and achieving technological breakthroughs. [Method] Building upon the definitions of key and core technology concepts, this study proposes a key and core technology identification method based on patent competitiveness index and Doc-LDA topic model. The metrics including topic strength, topic co-occurrence strength, and effective cohesion constraint coefficient is considered. [Results] Taking new energy vehicles as an empirical research example, a total of 10 key core technologies are identified, including fuel cell technology, solid-state power battery technology, high-efficiency high-density motor drive system, lightweight plastic and composite material technology, cellular communication technology, mechatronics coupling integration, multi gear transmission, vehicle operation technology, intelligent control technology, and autonomous driving technology. [Limitation] In the future, the scope of technical field analysis will be further expanded, aiming to discover universal principles and explore methods to refine the granularity of topics. [Conclusion] By utilizing the patent competitiveness index and the Doc-LDA theme model, the paragraph semantic relationship is considered and existing recognition methods are optimized.
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Published: 18 April 2024
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