## 5 Conclusions  
We propose a BERT-GCN model based on a strong link relation graph for industry classification of water use enterprises, which improves the accuracy of enterprise classification. We create a strong link relation graph based on the industry keywords extracted by _TF-IDF_, and extract co-word relation features by GCN. Then, we extract semantic features from the main business data collected by web crawlers. Finally, the semantic features and co-word relation features are connected to enhance the feature vector, and the classification results of the enterprise industry are obtained through a fully connected layer with multi-level constraints. The method was validated using two datasets from Jiaxing City and Zhuji City in Zhejiang Province. The experimental results showed that, compared with text classification methods such as TextCNN, BERT-FC, TextGCN, and Word2 Vec-GCN, the BERT-GCN, based on a strong link relation graph, can obtain more complete semantic features from low-quality data for enterprise classification. Through classification models, we can predict the missing industry information for water-using enterprises. This enables us to statistically analyze water demand and usage per industry, assess each industry's water use efficiency, and formulate more scientific water resource management policies and measures. However, this study still has several limitations, such as data sources and generalization, regional and industry applicability, and label uncertainty. Therefore, in future research, we plan to integrate more diverse enterprise-related information to construct multidimensional relationship graphs, collaborate with external data platforms, and standardize data. Meanwhile, due to the black box nature of deep learning models, although our method can extract rich features for the industry classification of water companies, users still cannot understand the basis of model decisions. The joint application of federated learning (FL) and explainable artificial intelligence (XAI) to alleviate data privacy and model interpretability issues is novel and timely. Therefore, in future research, we plan to combine FL and XAI to construct a federated learning framework to protect data privacy, while introducing interpretability techniques to analyze industry classification decision criteria for further interpretability. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Thresholds**} & \multirow{2}{*}{**\# of Words**} & \multicolumn{3}{c}{**Minor Categories**} & \multicolumn{3}{c}{**Major Categories**} \\ \cline{3-8}  & & _Precision_ & _Recall_ & _F1-Score_ & _Precision_ & _Recall_ & _F1-Score_ \\ \hline
0.01 & 275 & 71.94 & 68.24 & 68.73 & 93.43 & 93.13 & 92.94 \\
0.02 & 112 & 79.89 & 79.17 & 78.53 & 94.45 & 93.14 & 93.44 \\
0.03 & 61 & **84.64** & **83.34** & **83.41** & **96.63** & **96.19** & **96.08** \\
0.04 & 42 & 83.32 & 82.73 & 82.71 & 95.23 & 94.73 & 94.82 \\
0.05 & 32 & 81.34 & 79.72 & 80.18 & 94.12 & 93.33 & 93.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification accuracy with different _TF-IDF_ thresholds (%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**\# of Hidden**} & \multicolumn{3}{c}{**Minor Categories**} & \multicolumn{3}{c}{**Major Categories**} \\ \cline{2-7}  & _Precision_ & _Recall_ & _F1-Score_ & _Precision_ & _Recall_ & _F1-Score_ \\ \hline
16 & 83.81 & 81.94 & 81.78 & 95.56 & 95.37 & 94.72 \\
32 & 84.96 & 82.86 & 82.85 & 96.18 & 95.83 & 95.46 \\
64 & 85.31 & 83.03 & 83.12 & 96.34 & 96.06 & 95.94 \\
128 & **84.64** & **83.34** & **83.41** & **96.63** & **96.19** & **96.08** \\
256 & 85.45 & 83.15 & 83.04 & 96.25 & 96.04 & 95.89 \\
512 & 84.82 & 82.74 & 82.79 & 96.63 & 95.61 & 95.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification accuracy with different numbers of hidden units (%). **Author Contributions:** Conceptualization, J.X. ; data curation, B.Z. ; formal analysis, S.Y. and S.G.; funding acquisition, J.X. ; investigation, C.C. ; methodology, J.X., B.Z. and C.C.