###### Abstract
Creating a domain-specific lexicon for this area could critically enhance efforts in risk assessment, causal analysis, emergency decision making, risk monitoring, and early warning systems. Pre-training technology aims to develop general models using large datasets, which can be fine tuned for various downstream applications [21]. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model based on the Transformer architecture, which converts textual data into high-dimensional dense vectors [22]. BERT encodes semantic information of input texts using self-attention, generating word embeddings and processing them across multiple Transformer layers to capture contextual details, ultimately producing vectors for each word or sentence. These vectors encapsulate rich semantic and contextual details, enabling a better understanding of word polysemy and nuances across different contexts. The Chinese BERT model, pre-trained on a large corpus of Chinese text, has emerged as a versatile architecture capable of handling a wide range of natural language processing (NLP) tasks [23]. Chinese word embeddings as input features have proven effective in NLP applications and real-world challenges, including entity relation extraction, word similarity measurement, and disease identification. TF-IDF (Term Frequency-Inverse Document Frequency) is a widely used method for keyword extraction [24]. By calculating the TF-IDF value for each word, significant key words within a specific document can be identified [25]. Since its inception, TF-IDF has been extensively applied to the construction of domain-specific lexicons. Research indicates that TF-IDF effectively identifies keywords and excels in automatically generating domain-specific vocabularies. As text analysis techniques have advanced, TF-IDF has been optimized to enhance domain-specific lexicon accuracy, especially with large-scale corpora. Recent research has explored integrating TF-IDF with other NLP techniques to enhance domain-specific lexicon construction efficiency and effectiveness. This paper presents a domain-specific lexicon encompassing standard terminology, risk inventories, and emergency-related terms for natural gas pipeline networks. It quantifies the relevance of these terms and evaluates their interrelationships within the domain. Creating this domain lexicon offers several advantages, including improved precision in natural language processing systems' understanding of specialized texts. It also serves as a valuable resource for knowledge discovery and data mining. Additionally, the lexicon aids in feature engineering, enabling models to process and interpret domain-specific data effectively. Establishing a domain-specific lexicon for gas pipeline emergencies enhances management capabilities and supports rigorous cause analysis, risk assessments, and emergency response strategy development. The innovations of this study include the following:  
1. We developed a specialized domain-specific lexicon for emergencies in gas pipeline networks after extensively reviewing the literature and consulting with field experts. To our knowledge, this is the first lexicon specifically focused on this area. 2. We trained a Chinese pre-trained model specifically for emergencies in gas pipeline networks, successfully extracting semantic nuances from substantial domain-specific data. 3. We introduced a hybrid method that enhances the traditional TF-IDF technique with deep learning to incorporate domain knowledge more effectively. Initially, statistical methods filtered out less relevant terms, followed by deep learning to further develop and enhance the lexicon with detailed domain knowledge. This paper is divided into five sections. The first section outlines the necessity and significance of developing a specialized lexicon for gas pipeline emergencies. The second section details the materials and methods used. The third section presents the research findings and analysis. The fourth section discusses the practical implications of the research.