An empirical analysis of dynamic network model of international trade by using enterprise sample simulation and improved ANN algorithm

Abstract

Abstract With the support of enterprise intelligence system, an intelligent modeling method based on synthesizing various data sources and complex metabolic networks is adopted to provide technical support for the practical application of complex large-scale dynamic models. Academic research on international trade networks relies to a large extent on network models based on macro-national data and static analysis of international trade patterns, which are usually based on charts for trade flows and economic globalization. In this paper, we analyze the complex dynamic meta-network model of international trade based on improved neural network algorithm. Because BP neural networks run through the best neural network model that is constantly adjusted according to weighted values, the adjustment of neural network value is reduced, which may be an effective means to improve the efficiency of intrusion testing applications. The simulation results show that the model has good explanatory power. The internationalization of enterprise market as the main body of international business activities affects the establishment and development of a national or regional international trade network. Therefore, there is a need to study international trade networks using data at the micro level. This paper suggests an interdisciplinary approach to the study of international trade networks and a micro-level study of international trade networks. The model based on complex meta-network dynamic model elements, introduce the dynamic network representation, use temporary labels to define the network boundary characteristics.