Intelligence customs declaration for cross-border e-commerce based on the multi-modal model and the optimal window mechanism

This paper aims to study the intelligent customs declaration of cross-border e-commerce commodities from algorithm design and implementation. The difficulty of this issue is the recognition of commodity names, materials, and processing processes. Because the process of recognizing these three kinds of commodity information is similar, this paper chooses to identify the commodity name as the experimental research object. The algorithm in this paper is based on the premise of pre-clustering, using an optimal window mechanism to obtain the best word embedding vector representation. The Vision Transformer model extracts image features instead of traditional CNN models, and then text features are fused with image features to generate a multi-modal semantically feature vector. Finally, a deep forest classifier replaces the conventional neural network classifiers to complete the commodity name recognition task. The experimental results show that, for more than 600 different commodities on the 120,000 data records, the precision is 0.85, the recall is 0.87, and the F[Formula: see text] _score is 0.86. So, our algorithm can effectively and accurately recognize e-commerce commodity names and provide a new perspective on the research of e-commerce intelligence declarations.