Modeling and Simulation of Export Path Design for Digital Economy Products Based on Machine Learning Algorithms
Year: 2024
Type: conference-paper
Abstract: In the rapidly evolving landscape of the digital economy, understanding and optimizing export paths for digital products is crucial for enhancing competitiveness and market reach. This study presents a comprehensive framework for modeling and simulating export paths of digital economy products utilizing advanced machine learning algorithms. By integrating data from various sources, including market trends, consumer behavior, and regulatory environments, we develop predictive models that identify optimal export strategies. Our methodology employs techniques such as clustering, regression analysis, and neural networks to analyze complex datasets and uncover hidden patterns in export dynamics. The results demonstrate the efficacy of machine learning in forecasting demand, assessing market entry risks, and optimizing supply chain logistics. Additionally, we provide insights into the impact of external factors, such as geopolitical shifts and technological advancements, on export performance. This research not only contributes to the theoretical understanding of digital product exports but also offers practical guidelines for businesses seeking to navigate the global digital marketplace effectively. Future work will focus on refining the models through real-time data integration and exploring the role of emerging technologies in shaping export strategies.
Authors: Na Yao, Yang Liu, Nian Liu
Institution: Guangdong University of Science and Technology
Language: English
Subfield: Business and International Management
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4409095689