## 4 Experimental Results and Analysis
### Comparison with Baseline  
To verify the effectiveness of the proposed model, we compared it with the following baselines. (1) TextCNN: TextCNN is a model that uses convolutional neural networks for text classification. The core idea is to use convolutional neural networks to extract features from text data in text classification tasks, capture local word combination features by applying convolution operations to the text data, and then down sample through pooling layers to ultimately use these features for classification tasks. (2) BERT-FC: BERT-FC refers to an architecture that combines BERT models with fully connected layers, commonly used for text classification and other natural language processing tasks. BERT is used to extract text features, while fully connected layers are used to map the extracted features to the desired output space. (3) TextGCN: TextGCN takes the enterprise name and keywords as nodes, learns the representation of the nodes through the graph convolutional network, and then classifies the industries of water-using enterprises. (4) Word2 Vec-GCN: Word2 Vec-GCN is an architecture that combines the Word2 Vec word embedding method with graph convolutional networks. Tables 4 and 5 show the accuracy of several models in minor categories and major categories on the Xiuzhou and Zhuji datasets, respectively. Tables 4 and 5 present a comparison of the classification accuracy between the BERT-GCN model and baseline models. The accuracy in the tables is calculated from the average values of the five results, and the Kruskal-Wallis test was employed to assess the statistical significance of the differences in classification accuracy across the models. For both the Xiuzhou and Zhuji datasets, all the \(p\)-values are less than 0.05, indicating that the BERT-GCN model stably outperforms the other models in terms of classification accuracy. As shown in Tables 4 and 5, in terms of classification precision evaluation indicators, our method achieved classification precision of 98.18% and 96.63% for the major categories and 85.78% and 84.64% for minor categories in the Xiuzhou and Zhuji datasets, respectively, with the highest classification accuracy among the four algorithms. In the minor classification results for the Xiuzhou and Zhuji datasets, the \(F1\)-_score_ of our method improved by 38.46%, 29.96%, 13.05%, 7.11%, 42.84%, 29.20%, 11.68%, and 6.76% compared to TextCNN, BERT-FC, TextGCN, and Word2 Vec-GCN, respectively. These results show that the enhanced BERT-GCN model can classify enterprise industries more accurately and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Minor Categories**} & \multicolumn{3}{c}{**Major Categories**} \\ \cline{2-7}  & _Precision_ & _Recall_ & _F1-Score_ & _Precision_ & _Recall_ & _F1-Score_ \\ \hline TextCNN & 48.23 & 47.55 & 47.18 & 94.73 & 94.38 & 94.47 \\ BERT-FC & 57.87 & 56.12 & 55.68 & 95.47 & 95.14 & 95.04 \\ TextGCN & 75.36 & 73.45 & 72.59 & 96.68 & 96.56 & 96.47 \\ Word2 Vec-GCN & 80.37 & 79.59 & 78.53 & 97.64 & 97.33 & 97.16 \\
**BERT-GCN** & **85.78** & **85.70** & **85.64** & **98.18** & **98.00** & **97.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy comparison in Xiuzhou (%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Minor Categories**} & \multicolumn{3}{c}{**Major Categories**} \\ \cline{2-7}  & _Precision_ & _Recall_ & _F1-Score_ & _Precision_ & _Recall_ & _F1-Score_ \\ \hline TextCNN & 43.14 & 41.35 & 40.57 & 79.16 & 77.78 & 76.02 \\ BERT-FC & 55.46 & 55.59 & 54.21 & 83.13 & 85.71 & 85.63 \\ TextGCN & 73.03 & 72.80 & 71.73 & 85.94 & 85.18 & 84.77 \\ Word2 Vec-GCN & 80.69 & 78.95 & 76.65 & 88.52 & 86.67 & 86.45 \\
**BERT-GCN** & **84.64** & **83.34** & **83.41** & **96.63** & **96.19** & **96.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy comparison in Zhuji (%). can be applied to the task of enterprise industry classification.