Affiliations: [a]
School of Business, Nanjing Normal University, Nanjing, Jiangsu, China
| [b] CDP Group Limited, Shanghai (Global Headquarter), China
Correspondence:
[*]
Corresponding author. Zhen Zhen, School of Business, Nanjing Normal University, Nanjing, Jiangsu 210046, China. E-mail: zhenzhen98613@126.com.
Abstract: Technological innovation in manufacturing industry is a kind of R&D activity that produces new technologies, including input and output of technological innovation. In this paper, the authors analyze the lean production and technological innovation in manufacturing industry based on SVM algorithms and data mining technology. Data mining can discover novel, effective, potential and ultimately understandable data patterns from a deeper level, and encode the data to predict the development trend of enterprises. The machine learning support vector machine method is used to analyze and model the collected data. At the same time, we constructed a decision tree using random forest, and explained the significance of the training algorithm through the visualization results. The simulation results show that learning growth dimension and market dimension have the greatest impact on business model innovation. In the context of TEC, business model innovation must pay attention to market grasp and customer demand oriented, so as to improve the competitiveness of manufacturing enterprises.
Keywords: SVM Algorithms, data mining, manufacturing enterprises, science and technology level
