Deep Data Mining of the Characteristics of Enterprise’s Technology Development Trend

Changliang Wang ()
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Changliang Wang: School of Design and Art, Zhejiang Industry Polytechnic College, Shaoxing 312000, P. R. China

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 03, 1-19

Abstract: This paper studies a deep-seated data mining method for the development trend of enterprise technology. Technical distance, technical personnel and R & D investment are selected as the enterprise’s technical characteristics mined by the deep data mining method. The deep mining of enterprise’s technical characteristics is realised by defining mining objectives, data sampling, data exploration, data preprocessing, pattern discovery and prediction modelling of restricted Boltzmann machine. The mining results are used to analyse the impact of enterprise’s technical characteristics on the development trend. Ten science and technology enterprises are selected as the empirical analysis object. The empirical research results show that the three enterprise’s technical characteristics of technical distance, technicians and R & D investment have a great impact on the enterprise development trend. The results show that the method in this paper has certain practical application significance, and also provides a theoretical basis for enterprises to use technological innovation to occupy the market.

Keywords: Enterprise; technology development; trend characteristics; deep data mining; technical distance (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219649223500090

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