###### Abstract
This task is pivotal not only for comprehending domain-specific knowledge but also for tracking domain evolution, carrying significant theoretical and practical value [14]. The advancement of natural language processing technology has markedly diminished the need for manual involvement in creating these lexicons [15]. An interactive approach has been introduced in the field of terminology lexicon construction for creating user-centric lexicons aimed at text analysis [16]. This method leverages human-machine interaction to precisely capture user requirements and intentions, enhancing the lexicon's relevance and accuracy. The innovative algorithm can efficiently grasp an analyst's intent with minimal data, enhancing both the efficiency of the construc  
Figure 1: Number and proportion of emergencies in China’s gas pipeline networks, 2020–2023, compared to all gas-related accidents. tion process and the quality of the lexicon. Although this approach functions with minimal interactions, practical application limits on the number of interactions might impinge on the lexicon's comprehensiveness and scope. Deep learning has greatly impacted lexicon construction, evidenced by the development of a domain terminology lexicon using Chinese Wikipedia [17]. This approach integrates the Word2 vec model with Wikipedia's link structure, enriching and refining domain terminology representations. Neural networks have notably expanded the lexicon's scope and utility. The method's effectiveness was proven in the automotive sector, highlighting its strengths in creating domain-specific lexicons. While this approach performed well in the automotive sector, its adaptability across different fields requires further investigation and validation. Reducing human intervention represents a significant advancement in technology. A novel method has been introduced for building Chinese domain-specific vocabularies via new word recommendations [18]. This method ingeniously addresses new word discovery sparsity using enhanced mutual information and branch entropy techniques, improving low-frequency term identification. It significantly alleviates the need for expert reviews, streamlining the construction of domain lexicons via automated processes. The success of new word recommendations depends heavily on the quality and breadth of domain-specific corpora, with corpus selection and pre-processing being crucial. Domain lexicons are crucial for model optimization, as particularly demonstrated in news classification. Research shows that using bigram-based lexicons to classify unseen news data yields efficient and accurate results. Extending these lexicons to include bigrams and trigrams can marginally enhance accuracy [19]. Researchers have developed a lexicon learning algorithm using projected dual reconstruction for cross-domain recognition [20]. This approach creates class-specific sub-lexicons that maintain inherent connections between source and target domain data, incorporating label consistency constraints and classifier training. Despite its innovation, the algorithm's high time complexity and intricate optimization present challenges to broader applicability. In conclusion, significant progress has been achieved across various research areas, including the development of domain-specific lexicon construction methods, the enhancement of human-computer interactions, the reduction of human labor, the integration of deep learning techniques, and the optimization and evaluation of lexicons to improve classification model efficacy. Despite these advancements, several challenges remain, notably the high computational demands, stringent data pre-processing requirements, and the limited cross-domain applicability of these methods. In the context of emergencies in gas pipeline networks--a sector that heavily relies on human input--the lack of a tailored lexicon represents a significant gap.