Evolution Mechanism of Advanced Equipment Manufacturing Innovation Network Structure from the Perspective of Complex System

• Main Authors: Jianbo Wang, Xing Cao
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2021/6610767

Our country’s equipment manufacturing industry ranks among the best in all developing countries, but compared with developed countries, there is still a long way to go. It is not only the backwardness of various technologies, but also the interference of other countries. Although our country's equipment manufacturing industry is not as advanced as the advanced technology of developed countries, we still have to stick to our original aspirations, do not underestimate ourselves, and be good at absorbing and learning from the strengths of others to make up for our own weaknesses. While not working behind closed doors and while absorbing technology from other countries, we can make use of our strengths to make up for our weaknesses and develop our own industrial technology. This paper studies the evolution trend of innovation network structure and at the same time studies the evolution mechanism of advanced equipment manufacturing innovation network structure from the perspective of complex systems. The explained variable in this article is green total factor productivity. The variable adopts the Malmquist–Luenberger global super-efficiency index model. There are two main explanatory variables. One is the heterogeneity that affects the efficiency of industrial evolution, including factor heterogeneity, structural heterogeneity, and environmental heterogeneity, and the other is the interaction term of equipment manufacturing specialization agglomeration degree dummy variable multiplied by factor heterogeneity. The regional economic development level is added to the model as a control variable. In the selection of measurement indicators, the per capita GDP is used as the control variable. The experimental results show that each sample is tested in pairs, and the standard error level of the mean is 0.018, which is less than 0.05, indicating that the efficiency of the equipment manufacturing industry’s economic correlation spatial network has a significant impact on the overall economic development level of the industry. The reduction in spur helps to increase economic output.