The rapid growth of e-commerce has placed considerable pressure on customs
representatives, prompting advanced methods. In tackling this, Artificial
intelligence (AI) systems have emerged as a promising approach to minimize the
risks faced. Given that the Harmonized System (HS) code is a crucial element
for an accurate customs declaration, we propose a novel multimodal HS code
prediction approach using deep learning models exploiting both image and text
features obtained through the customs declaration combined with e-commerce
platform information. We evaluated two early fusion methods and introduced our
MultConcat fusion method. To the best of our knowledge, few studies analyze the
featurelevel combination of text and image in the state-of-the-art for HS code
prediction, which heightens interest in our paper and its findings. The
experimental results prove the effectiveness of our approach and fusion method
with a top-3 and top-5 accuracy of 93.5% and 98.2% respectively

通过深度学习模型，我们提出了一种新的多模态海关编码预测方法，利用图像和文本特征相结合的海关申报信息和电子商务平台信息。实验证明了我们的方法和融合方法的有效性，分别具有 93.5% 和 98.2% 的前三和前五准确率。