Abstract
Traditional market analysis tools primarily rely on unidimensional data, such as historical trading records and price trends. However, these data are often insufficient to reflect the actual state of the market fully. This study introduces a meta-learning-based (MLB) multimodal data fusion approach to optimize feature extraction and fusion strategies, addressing the complexity and heterogeneity inherent in international trade market data. Initially, the mel-frequency cepstral coefficients (MFCC) method is employed to transform the original audio signal into more discriminative spectral features. For image data, the convolutional block attention module (CBAM) is incorporated to capture both channel-wise and spatial attention, thereby improving the model's ability to focus on market-relevant information. In the feature fusion stage, a meta-learning bidirectional feature pyramid network (ML-BiFPN) is proposed to refine the interaction of multi-scale information via a bidirectional feature pyramid structure. An adaptive weighting mechanism is employed to adjust the feature fusion ratio dynamically. Experimental results demonstrate that the proposed multimodal data fusion model, ML-BiFPN under meta-learning, significantly outperforms existing methods in prediction performance. When tested on the publicly available Trade Map dataset, the average accuracy improves by 9.37%, and the F1-score increases by 0.0473 compare to multilayer perceptron (MLP), achieving a prediction accuracy of 94.55% and an F1-score of 0.912. Notably, under small sample conditions, the model's advantage becomes even more pronounced, with an average precision (AP) improvement of 2.79%. These findings have significant implications for international trade market forecasting and decision-making, providing enterprises with a more comprehensive understanding of market dynamics, enhancing forecasting accuracy, and supporting scientifically informed decision-making to gain a competitive edge in the marketplace.