## 1 Introduction  
With the continuous development of economy and society, China's demand for water resources continues to grow, and the contradiction between supply and demand is becoming increasingly prominent [1, 2, 3]. To improve the efficiency of water resource utilization, the water resources department has put forward refined management measures for enterprise water use, and the aim of water industry classification is to improve the efficiency and accuracy of water resource management [4]. By classifying water consumption, analyzing industrial water consumption data, collating water consumption information from the water supply network, and supporting the closed-loop analysis of water consumption data, the water demand and usage of various industries can be better understood, and more scientific water resource management policies and measures can be formulated [5, 6]. However, corporate water use data collected from grassroots units are often of low quality, with industry information frequently missing, making it difficult to accurately obtain water use data across industries. By analyzing the original data, we find that there is a high correlation between enterprise name and industry, and the enterprise industry type can be determined according to the semantic features in the text of an enterprise's name. However, the sparsity of features in short texts such as enterprise names and the significant heterogeneity of water usage amongst enterprises pose a huge challenge to extracting effective semantic features from water using enterprise names. For example, differences in enterprise structure and organizational forms lead to inconsistent naming rules, which interfere with the extraction of core business features by models. The dynamic differences in regional water use patterns result in different semantics for words in the same industry, leading to distorted contextual representation. This complexity makes it difficult for existing classification methods based on manual rules or simple statistics to adapt to dynamic changes, so determining how to classify industries from these texts is a challenge in current research. In recent years, the vigorous development of deep learning has brought new opportunities to solve the problem of text classification [7, 8]. On the one hand, deep learning methods can automatically mine essential features in text and capture deep semantic representation information from text data, avoiding the process of the manual design of rules and features; on the other hand, deep learning models can map the information of different modes into vector space, alleviating the problem of information shortage faced by discriminating from a single datum. For example, in 2013, Google proposed Word2 Vec [9], which uses the context of words to transform text data into structured and computable dense vectors, greatly improving the performance of text classification. In 2018, the University of Washington proposed ELMo [10], which uses a multilayer bidirectional long short-term memory network (LSTM) to model the syntactic and semantic features of text. With the rise of transformers in recent years [11], a series of pre-trained language models such as BERT [12] can now be used to learn a general representation of text from a massive corpus, significantly improving the accuracy of downstream classification tasks. However, short texts such as enterprise names are characterized by fewer words, more differences, and sparse features, which lead to many problems in text segmentation, text representation, feature extraction, and classification model building. Traditional models based on convolutional neural networks (CNNs), LSTM, and transformers mainly extract contextual information from sequence text for classification [13, 14, 15]. However, it is worth noting that text contains not only sequence features, but also rich graph structure information, such as syntax and co-words [16].