## 5 Conclusions and Discussion  
In this paper, we propose a semi-structured short text classification model, EBLCS, to address the HS code classification problem for import and export goods. The EBLCS model first utilizes ERNIE to achieve an accurate and efficient representation of a goods description text. It then employs BiLSTM to capture the global contextual features of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset Size** & **Weight-P** & **Weight-R** & **Weight-F1** & **Accuracy** \\ \hline \multirow{4}{*}{BERT-BLCS} & 20\% & 0.7559 & 0.7549 & 0.7496 & 0.7549 \\  & 50\% & 0.8079 & 0.8043 & 0.8035 & 0.8043 \\  & 80\% & 0.8519 & 0.8480 & 0.8483 & 0.8480 \\  & 100\% & 0.8642 & 0.8639 & 0.8632 & 0.8639 \\ \hline \multirow{4}{*}{EBLCS} & 20\% & **0.7753** & **0.7726** & **0.7704** & **0.7726** \\  & 50\% & **0.8315** & **0.8283** & **0.8277** & **0.8283** \\ \cline{1-1}  & 80\% & **0.8654** & **0.8642** & **0.8641** & **0.8642** \\ \cline{1-1}  & 100\% & **0.8751** & **0.8741** & **0.8738** & **0.8741** \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Weight-P** & **Weight-R** & **Weight-F1** & **Accuracy** \\ \hline BiLSTM & 0.7729 & 0.7670 & 0.7649 & 0.7670 \\ BLCS & 0.8266 & 0.8194 & 0.8196 & 0.8194 \\ ERNIE-BiLSTM & 0.8501 & 0.8496 & 0.8489 & 0.8496 \\ EBLC & 0.8611 & 0.8658 & 0.8651 & 0.8658 \\ EBLS & 0.8647 & 0.8638 & 0.8634 & 0.8638 \\ EBLCS & **0.8751** & **0.8741** & **0.8738** & **0.8741** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation experiment results on the HSD dataset.  
text. The integration of channel attention and spatial attention further enhances the model's ability to identify and emphasize key information, assigning greater weights to important words and dimensions in the word embedding. The final feature vector is input into a softmax classifier to obtain classification results, significantly improving overall accuracy. Experimental results on real customs data validate the effectiveness of EBLCS.  
Future work will focus on optimizing the model to reduce computational overhead without compromising classification accuracy. This could involve exploring more efficient network architectures, pruning techniques, or other forms of model compression.  
In conclusion, the EBLCS model offers a robust approach for HS code classification of import and export goods, demonstrating significant improvements in accuracy and effectiveness. With further optimization and exploration, it has the potential to become a valuable tool for customs administrations and other applications involving text classification.  
Data curation, L.H.; validation, B.L.; writing coach, J.Z.; reviewing, L.S.; validation, M.L.; writing and editing, L.H.; Funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China: 2021 YFC3340501 and Beijing Postdoctoral Research Foundation: 2024-zz-062.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.