Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230200086-7.doi: 10.11896/jsjkx.230200086

• Artificial Intelligence • Previous Articles     Next Articles

Inductive Interactive Network Model for Customs Import and Export Commodity Tax Rate Detection

WU Anqi1, CHE Chao1, ZHANG Qiang1,2, ZHOU Dongsheng1   

  1. 1 Key Laboratory of Advanced Design and Intelligent Computing,Ministry of Education,Dalian University,Dalian 116622,China
    2 School of Computer Science and Technology,Dalian University of Technology,Dalian 116086,China
  • Published:2023-11-09
  • About author:WU Anqi,born in 1997,postgraduate.His main research interests include natural language processing and so on.
    ZHANG Qiang,born in 1971,Ph.D,professor.His main research interests include biological computing and artificial intelligence,big data analysis and processing.
  • Supported by:
    111 Project(D23006),LiaoNing Revitalization Talents Program(XLYC2008017),National Natural Science Foundation(62076045,62102058) and Educational Department of Liaoning Provincial(LJKFZ20220290).

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Abstract: The traditional way of examining the tax rate of manual goods in China Customs has problems such as low efficiency,inconsistent judgment basis and low precision.Using text classification method to automatically determine the tax rate of commodity classification can effectively reduce the risk of customs tax rate.However,when classifying customs commodity data,commodity categories are hierarchical.Many sub-categories under the same category have highly similar commodity descriptions,which brings great challenges to commodity classification.Therefore,an inductive interactive network model is proposed,and inductive and interactive guidance modules are added on the basis of BERT and CNN.In the induction module,the dynamic routing algorithm is used to perform iterative operation on the features extracted by CNN,which can effectively solve the problem of adjacent feature fusion and redundancy.At the same time,in order to solve the feature similarity problem between different subcategories and improve the classification performance,the interactive guidance module is introduced,which is mainly to interact the feature information extracted by the induction module with [CLS] classification vector.Experiment is carried out on the real customs data set,and the results show that the method can achieve good results,the accuracy is up to 92.98%,and the performance is obviously better than that of each baseline model.

Key words: China Customs, Tax rate detection, Inductive interaction, Dynamic routing, Interactive guidance

CLC Number: 

  • TP391
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