## 1 Introduction
Many researchers use graph convolutional networks (GCNs) to update the representation of nodes by iteratively aggregating the neighbor information of nodes, and capturing the graph structure information for text classification, thus achieving superior performance. For example, graph-CNN [17] first converts text into word graphs, uses GCN to represent the word graphs, and finally obtains document labels through a classifier. TextGCN [18] takes words and documents as nodes at the same time and constructs a heterogeneous word text graph to represent text data and realize the classification of documents. Enterprise names are typically composed of four elements: administrative division, brand name, industry or business characteristics, and organizational form. As a result, enterprises in related industries may share identical business characteristics words, which can be harnessed to construct a graph, thereby enabling the application of a GCN for classification. However, due to the interference of administrative division and organizational form, direct composition can easily be used to form a dense map, which affects the accuracy of industry identification. Therefore, it is difficult to directly apply the existing text classification method to the field of water enterprise classification. Based on this, we propose a bidirectional encoder representations from transformers and graph convolutional network (BERT-GCN) model based on a strong link diagram for the classification of water use enterprises to improve the accuracy of industry identification. First, to solve the problem of the construction of the co-word relation between enterprises, we designed a co-word relation graph based on the industry keywords extracted by word frequency-inverse document frequency (_TF-IDF_), and extracted the co-word relation features using the graph convolutional network (GCN). Then, considering the low word count and semantic information of enterprise names, we used a web crawler to collect the main business data of the enterprises from Aiqicai (an enterprise credit query tool launched by Baidu) as supplementary information, and extracted semantic features from the pre-trained language model BERT. Finally, we connected the co-word relation features and semantic features and obtained the classification results of minor categories and major categories via the fully connected layers. The main contributions are as follows:  
(1) The fusion and utilization of multi-source information improve classification accuracy. We construct a co-word relation graph to extract the co-word relation features. At the same time, a web crawler is used to extract the main business information of enterprises as supplementary data, and BERT is used to extract semantic features from that supplementary data. This method makes comprehensive use of multi-source information and can fully integrate the characteristics of many aspects to categorize enterprise industries. (2) A strong link diagram is constructed. Based on the statistics of naming preferences of enterprises in different industry categories, _TF-IDF_ is used to select words that can represent the characteristics of industry categories as co-words, and a strong link relation graph is constructed to remove co-words that contribute little to the final classification task, thus improving the precision of the industry classification of water use enterprises. (3) The short text classification method is aimed specifically at the problem of the industry categorization of water enterprises. To verify the reliability and effectiveness of the proposed method, we utilize two real data sets from Xiuzhou District of Jiaxing City and Zhuji City. Compared with TextCNN, BERT-FC, TextGCN, and Word2 Vec-GCN, the classification method we propose has the best performance in terms of precision, recall, and _F1-score_. It has important practical significance for regional water management. The following sections of this paper are organized as follows: Section 2 describes the current research in the field.