## 2 Materials and Methods  
### Dataset and Data Preprocessing  
This study utilizes chemical production accident investigation reports as the primary corpus for analysis, which experts within the safety field make through research, analysis, and writing after an accident. The official websites of China's Ministry of Emergency Management (MEM) and provincial and municipal emergency management departments have corresponding accident investigation reports. Several websites in China collect and organize accidents regarding chemical production systems, such as the Safety Management Network, China Chemical Safety Association, and Chemical Safety People. To establish robust data provenance, this study downloaded the chemical-related accident investigation reports from these websites from 2013 to 2022, totaling 843 articles. After implementing data cleaning procedures including deduplication and quality filtering, we obtained a final corpus of 514 chemical industry accident reports. The classified distributions of accident severity levels and incident types are visualized in Figures 2 and 3. Figure 2 shows that most accidents are general and more significant. Though these accidents caused relatively few casualties or damage, they occur with a high frequency and can still severely impact chemical production. Figure 3 reveals that explosions account for more than 50% of all accidents, while poisoning and asphyxiation incidents represent the second most common type. This pattern primarily stems from the widespread use of combustible, explosive, toxic, and hazardous gases in chemical manufacturing processes, which frequently lead to either mixed-gas explosions or worker exposure resulting in poisoning/asphyxiation cases. To prevent data redundancy, reduce the workload of text analysis, and minimize the interference of irrelevant information on the analysis, this study further screened out the \"Accident History\" and \"Accident Cause Analysis\" in each accident investigation report,which served as the initial corpus for the analysis of this paper. This study further screened out \"accident history\" and \"accident cause analysis\" from each accident investigation report as the initial corpus for this paper. Data preprocessing is an essential yet labor-intensive step, as the raw corpus often includes irregular or irrelevant terms. A key objective involves segmenting the original Chinese text--similar to how English text is divided by spaces--to facilitate subsequent analysis. The text segmentation tool used in this study is the Jieba splitter of python3. Linguistic analysis reveals that the causative factors in chemical accidents predominantly manifest as either nominal phrases or verb-noun constructions, indicating their syntactical simplicity [13], so only common nouns, organization names, other proper names, everyday verbs, and gerunds are selected during the segmentation so that Jieba segmentation will automatically eliminate the rest of the lexical segmentation results during the segmentation. Jieba principles contain three dictionaries: a domain dictionary, a synonym dictionary, and a deactivation dictionary. 1. Domain Dictionary: Although the Jieba system comes with a dictionary for splitting words that contain the most common words (e.g., reactor, piping, etc. ), current natural language processing systems exhibit significant limitations in recognizing domain-specific terminology, particularly technical terms such as 'distillation tower', 'intermediate chamber','steam valve', and 'gas detector' that are essential for accurate  
Figure 3: Percentage of different accident types. Figure 2: Number of accidents of different severity. process industry documentation analysis. When these words are encountered, the Jieba segmentation system may split the entire proprietary word into two or more words, thereby destroying words that would otherwise be highly indicative of safety risk factors.