## 4 Experiments and Results
### Model Comparison  
To comprehensively demonstrate the effectiveness of the proposed model, a variety of text classification methods were selected for comparative experiments. These methods include TextCNN, BiLSTM, FastText [26], Transformer, BERT, ERNIE-CNN, and ERNIE-BiLSTM, among others. The experimental results for these models are presented in Table 3.  
A comparison of the experimental results shows that the EBLCS model outperforms all baseline models across all indicators, achieving a classification accuracy of 87.74%. This verifies its effectiveness for the task of HS code categorization of import and export goods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Value** & **Hyperparameter** & **Value** \\ \hline Hidden Size & 768 & Learning rate & \(5\times 10^{-5}\) \\ Max length & 64 & Dropout & 0.1 \\ Epoch & 10 & Optimizer & Adam \\ Batch size & 128 & Activation function & ReLU \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameter configuration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Weight-P** & **Weight-R** & **Weight-F1** & **Accuracy** \\ \hline TextCNN & 0.7659 & 0.7546 & 0.7523 & 0.7546 \\ BiLSTM & 0.7729 & 0.7670 & 0.7649 & 0.7670 \\ FastText & 0.8122 & 0.8090 & 0.8074 & 0.8090 \\ Transformer & 0.7607 & 0.7578 & 0.7553 & 0.7578 \\ BERT & 0.8487 & 0.8465 & 0.8462 & 0.8465 \\ ERNIE & 0.8540 & 0.8530 & 0.8526 & 0.8532 \\ ERNIE-CNN & 0.8471 & 0.8435 & 0.8433 & 0.8435 \\ ERNIE-BiLSTM & 0.8501 & 0.8496 & 0.8489 & 0.8496 \\ EBLCS & **0.8751** & **0.8741** & **0.8738** & **0.8741** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental results on the HSD dataset. Compared to TextCNN, BiLSTM, and Transformer, the accuracy of the proposed model improves by more than 10%. When compared to FastText, the accuracy is enhanced by 6%, and it exceeds BERT and ERNIE by over 2%. BERT and ERNIE utilize the Transformer structure for bidirectional language modeling, allowing them to leverage contextual information to dynamically adjust word representations. This approach alleviates the issue of polysemy and enables the models to learn more general semantic representations through self-supervised training on large-scale corpora, making them superior to word2 vec-based models. However, compared to BERT, ERNIE employs entity- and phrase-level masking for Chinese, and the integration of external knowledge enhances its ability to represent complex text, significantly improving its handling of polysemous words and nuanced language [27]. We used the ERNIE model to represent goods text, achieving more effective text representation vectors. Experimental results indicate that ERNIE performs better than both Word2 Vec and BERT in this regard. ERNIE-CNN shows a decrease of 0.97% in accuracy compared to ERNIE. This drop is attributed to TextCNN's convolutional operations, which extract local semantic information but may lead to over-fusion of features when handling goods text, resulting in reduced classification accuracy. Similarly, ERNIE-BiLSTM experiences a decrease of 0.36% in accuracy. This is because the ERNIE model has already performed rich representation learning on the text data. While BiLSTM can capture features from other dimensions, it may also result in the loss of some feature information from the original word vectors. However, despite [PERSON] et al. [6] using an auxiliary network to reduce semantic fusion in goods classification, it still has limitations in capturing global dependencies within the text. In this paper, we splice the output of BiLSTM with the original feature vectors to capture new important features while retaining the original ones. And we amplify the key features by combining channel attention and spatial attention.