The increasingly serious imbalance between the supply and demand of water resources necessitates the establishment of a scientific and reasonable comprehensive evaluation method for industrial water use efficiency (WUE). In this study, a general method for industrial WUE evaluation on an enterprise scale was proposed by combining the analytic hierarchy process (AHP), entropy weight method (EWM), and self-organizing map (SOM), and it was tested in several areas of Zhejiang Province, China. The results show that the composite indexes generated using the AHP and EWM were different and were employed as the input of the SOM to divide enterprises into four categories. Most enterprises were classified as Class A, with a relatively high WUE, accounting for 82.5% of the total, while those in Class D, with a relatively low WUE, only accounted for 0.5% of the total. Furthermore, the differences in WUE for industry classification and spatial distribution were also analyzed. The classification results of several industries were more diverse, especially for those industries in which water plays an important role in production. Moreover, the spatial distribution of WUE classifications also implied that the clustering of enterprises has a positive effect on the improvement in WUE. In other words, it is feasible to improve WUE through industry clustering and sub-industry management. In summary, a comprehensive, detailed evaluation of industrial WUE was conducted on an enterprise scale, which can also be applied to other areas and used as a reference for local water resource managers for formulating targeted policies.

Due to the poor quality of current water consumption data and the frequent absence of industry information, accurately calculating the water consumption for different industries is difficult. Therefore, we propose a BERT-GCN model based on a strong link graph for classification within the water industry. First, we constructed a co-word relation graph based on the typical industry characteristics keywords extracted by the TF-IDF and extracted co-word relation features using a graph convolutional network (GCN). Then the web crawler was utilized to collect the main business data of the enterprise as additional information, and the semantic features were extracted from the supplementary information by the pre-trained language model BERT. Finally, we connected the semantic features with the co-word relation features to obtain the enhanced feature vector of the enterprise for the classification of the enterprise through the full connection layer. The experimental results in Xiuzhou District and Zhuji City show that compared with TextCNN, BERT-FC, TextGCN and Word2Vec-GCN models, the BERT-GCN has the best performance in classification evaluation indicators of precision, recall and F1-score. The relevant research provides technical and theoretical guidance for the government to carry out dynamic, rapid and accurate management of the water conservancy industry.