Article: Cross-border trade export prediction based on reinforcement learning and multimodal data Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.36 pp.1 - 16 Abstract: Cross-border trade export forecasting is important for enterprises to optimise resource allocation. However, existing prediction methods have the problem of insufficient single modal feature extraction, for this reason, this paper first optimises the reinforcement learning (RL) algorithm based on multilevel strategy and multilevel reward (MSRL). Then CNN, Doc2Vec model, and improved ResNet152 model were used to extract static variable features, comment text features, and image features of cross-border trade export sales volume, respectively, and a hierarchical attention mechanism was designed to fuse multimodal features. The hyperparameters of the BiGRU model are optimised using MSRL (MSRL-BiGRU), and the fusion features are input into MSRL-BiGRU, which efficiently and automatically searches for the optimal strategy and reduces the prediction error. The experimental results show that the proposed method improves the coefficient of determination R<SUP align="right"><SMALL>2</SMALL></SUP> by 4.84-18.67%, which can realise the accurate prediction of cross-border trade export sales. Inderscience Publishers - linking academia, business and industry through research

Title: Cross-border trade export prediction based on reinforcement learning and multimodal data

Authors: Yang Yu; Ziwen Sun; Jiachen Li

Addresses: School of Modern Finance, Jiaxing Nanhu University, Jiaxing, 314001, China ' National University Science Park, Hangzhou Normal University, Hangzhou, 311121, China ' School of Economics, Fudan University, Shanghai, 200433, China

Abstract: Cross-border trade export forecasting is important for enterprises to optimise resource allocation. However, existing prediction methods have the problem of insufficient single modal feature extraction, for this reason, this paper first optimises the reinforcement learning (RL) algorithm based on multilevel strategy and multilevel reward (MSRL). Then CNN, Doc2Vec model, and improved ResNet152 model were used to extract static variable features, comment text features, and image features of cross-border trade export sales volume, respectively, and a hierarchical attention mechanism was designed to fuse multimodal features. The hyperparameters of the BiGRU model are optimised using MSRL (MSRL-BiGRU), and the fusion features are input into MSRL-BiGRU, which efficiently and automatically searches for the optimal strategy and reduces the prediction error. The experimental results show that the proposed method improves the coefficient of determination R² by 4.84-18.67%, which can realise the accurate prediction of cross-border trade export sales.

Keywords: cross-border trade export forecasting; reinforcement learning; multimodal fusion; hierarchical attention mechanism; BiGRU model.

DOI: 10.1504/IJICT.2025.149053

International Journal of Information and Communication Technology, 2025 Vol.26 No.36, pp.1 - 16

Received: 24 May 2025
Accepted: 22 Aug 2025
Published online: 10 Oct 2025 *

Title: Cross-border trade export prediction based on reinforcement learning and multimodal data

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