The related information on smuggling crimes exists extensively in various media, with multiple data sources. Anti-smuggling intelligence faces the contradiction between the explosive growth of data size and high-efficiency intelligence judgment. Considering the current characteristics of smuggling activities, it is urgent to obtain knowledge from multi-source case data. Aiming to explore a smuggling knowledge acquisition algorithm based on deep learning, this study proposed an anti-smuggling knowledge representation model with both temporal and spatial characteristics and a knowledge-driven anti-smuggling intelligent judgment method. By combining two means, data, information, knowledge, and intelligence were effectively fused via the Term Frequency-Inverse Document Frequency (TF-IDF) technique and Levenshtein distance algorithms, promoting deep mining and application of anti-smuggling big-data resources and enhancing both automation and intelligence levels in anti-smuggling intelligence judgment.
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Yang et al. (2025) studied this question.
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