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

C. Wang
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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.
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企业技术发展趋势特征的深度数据挖掘
本文针对企业技术的发展趋势,研究了一种深层次的数据挖掘方法。选取技术距离、技术人员和研发投入作为深度数据挖掘方法挖掘的企业技术特征。通过定义挖掘目标、数据采样、数据探索、数据预处理、模式发现和受限玻尔兹曼机预测建模,实现企业技术特征的深度挖掘。利用挖掘结果分析了企业技术特征对发展趋势的影响。选取10家科技型企业作为实证分析对象。实证研究结果表明,技术距离、技术人员和研发投入这三个企业的技术特征对企业发展趋势有较大影响。结果表明,本文方法具有一定的实际应用意义,也为企业利用技术创新占领市场提供了理论依据。
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