Affiliation:
1. School of Economics and Management, Inner Mongolia Agricultural University, Hohhot 010010, China
2. School of Economics and Management, Ningbo University of Technology, Ningbo 315211, China
Abstract
This study deeply integrates multimodal data analysis and big data technology, proposing a multimodal learning framework that consolidates various information sources, such as user geographic location, behavior data, and product attributes, to achieve a more comprehensive understanding and prediction of consumer behavior. By comparing the performance of unimodal and multimodal approaches in handling complex cross-border e-commerce data, it was found that multimodal learning models using the Adam optimizer significantly outperformed traditional unimodal learning models in terms of prediction accuracy and loss rate. The improvements were particularly notable in training loss and testing accuracy. This demonstrates the efficiency and superiority of multimodal methods in capturing and analyzing heterogeneous data. Furthermore, the study explores and validates the potential of big data and multimodal learning methods to enhance customer satisfaction in the cross-border e-commerce environment. Based on the core findings, specific applications of big data technology in cross-border e-commerce operations were further explored. A series of innovative strategies aimed at improving operational efficiency, enhancing consumer satisfaction, and increasing global market competitiveness were proposed.
Funder
Inner Mongolia Natural Science Foundation
Interdisciplinary Research Fund of Inner Mongolia Agricultural University
Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region
National Key R&D Program of China
National Social Science Fund of China Post-funding Project
China Society of Logistics and China Federation of Logistics & Purchasing General Research Project
Inner Mongolia Autonomous Region Graduate Education Teaching Reform Project
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