In the customs good declaration scenarios， a classification model needs to be used to categorize the goods into uniform Harmonized System （HS） codes. However， the existing customs good classification models ignore the location information of words in the text to be classified， while the HS codes are in tens of thousands， which leads to problems such as class vector sparsity and slow convergence of the model.To address the above problems， a classification model based on Hierarchical Multi-task Bidirectional Encoder Representation from Transformers （HM-BERT） was proposed by combining the manual hierarchical classification strategy in real business scenarios and making full use of the hierarchical structure feature of HS codes. In one aspect， the dynamic word vector of Bidirectional Encoder Representation from Transformers （BERT） model was used to obtain the location information in the text of customs declaration goods. In other aspect， the accuracy and convergence of categorization were improved by making full use of the category information of different levels of HS codes to perform multi-task training of BERT model. In the effectiveness verification of the proposed model on the 2019 customs declaration dataset of a domestic customs service provider， HM-BERT model improves 2 percentage points in accuracy with faster training speed compared to BERT model， and improves 7.1 percentage points in accuracy compared with H （Hierarchical）-fastText. Experimental results show that HM-BERT model can effectively improve the classification effect of customs declaration goods.