International Trade Partner Selection Model and Its Influencing Factors Based on Machine Learning

Abstract

Iinternational trade partner selection model and its influencing factors based on machine learning represents a significant advancement in global business strategies. By harnessing the capabilities of machine learning algorithms, this model analyzes a multitude of variables such as market trends, economic indicators, and partner attributes to identify optimal trade partners for businesses. Factors such as geographical proximity, market stability, and cultural compatibility are incorporated into the model to provide data-driven insights into partner selection decisions. This paper investigates the application of Stacked Random Field Machine Learning (SRF-ML) in the domain of international trade partner selection. The selection of suitable trade partners is a crucial aspect of global trade operations, influencing the efficiency and success of business ventures. Traditional approaches to partner selection often rely on subjective assessments or simplistic models, which may overlook important factors and lead to suboptimal decisions. In contrast, SRF-ML offers a powerful framework for analyzing complex datasets and making informed predictions about partner suitability. Through the integration of multiple layers of random field models, SRF-ML can effectively capture intricate relationships between various attributes and provide more accurate assessments of partner compatibility. In this paper explore the performance of SRF-ML models across different datasets and scenarios, considering factors such as market size, economic stability, and logistical capability. The results demonstrate the superior performance of SRF-ML compared to traditional machine learning approaches, highlighting its potential to revolutionize partner selection processes in the realm of international trade. The results demonstrate the superior performance of SRF-ML compared to traditional machine learning approaches, with accuracy improvements ranging from 5% to 10%. By leveraging advanced feature sets and model configurations, SRF-ML enables decision-makers to make more informed and strategic decisions, ultimately enhancing the efficiency and effectiveness of global trade operations.