Machine Learning-Driven Social Network Analysis of Logistic Standardization Development

Logistics has been developing for nearly a hundred years. Over time, the industry is booming and covering more aspects. In the process of development, many countries also began to summarize the experience of logistics, formulate standards, and restrain and regulate the behavior of enterprises. The formulation of international standards can play a coordinating role, collect information, make logistics develop in a better direction, and play a more important role in the world. This paper applied the social network analysis method and finds that the standards of each country are difficult to integrate as international standards, and the construction of the standard system has been obstructed. This paper will give three possible solutions to overcome those defects. Additionally, this study integrates machine learning algorithms such as Random Forest and Support Vector Machines (SVM) to analyze the correlation between national logistics standards and their adoption in international frameworks. These algorithms enhance the predictive accuracy of standardization barriers and provide data-driven insights for future policy formulation.