Application of BERT-GCN Model Based on Strong Link Relation Graph in Water Use Enterprise Classification

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.