Study of the Competitive Landscape of Electricity Intellectual Property Based on Textual Analysis
Pages 645 - 650
Abstract
Taking the core journals and patent information of electric power specialty in China in the past five years as the data source of text processing, the text information of electric power intellectual property rights is divided into a series of phrases representing separate meanings by using jieba. The deactivated words in the phrases that have nothing to do with the electric power industry are eliminated, and the phrases are divided into 26 categories according to the word aggregation characteristics of the electric power industry. By inputting the electric power text into the text classification algorithm fasttext, the degree of relevance of each electric power text information to each category is derived. Subsequently, the primary evaluation system of the intellectual property competitiveness evaluation system is constructed, and the evaluation indexes of the power competitiveness evaluation are derived through the group decision-making feature root algorithm, and the entropy method is applied to solve the weight coefficient value of the evaluation indexes, analyze the distribution situation of some power subjects in the 26 categories of the power industry, and then take the power units of a certain province as an example, to further demonstrate the proposed competitiveness evaluation system based on big data mining.
References
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Index Terms
- Study of the Competitive Landscape of Electricity Intellectual Property Based on Textual Analysis
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December 2023
917 pages
ISBN:9798400716669
DOI:10.1145/3659211
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Association for Computing Machinery
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Published: 29 May 2024
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BDEIM 2023
BDEIM 2023: 2023 4th International Conference on Big Data Economy and Information Management
December 8 - 10, 2023
Zhengzhou, China
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