## 3 Results
### Association Rule Analysis  
The accident level information is added to the Boolean dataset of chemical enterprise accident risk causation information to obtain an adaptive data format for association rule mining. In this paper, the accident level is categorized into general accidents (G), significant accidents (L), and significant and above accidents (MS), which corresponds to each accident investigation report and combines with the Boolean dataset of accident risk causation information of chemical enterprises obtained above to obtain the primary dataset of association rule mining, as detailed in Table 6.  
Each entry in Table 6 corresponds to a textual record from an accident investigation report, i.e., an accident, and a \"1\" indicates that the risk factor in the corresponding column appeared in the accident. In contrast, a \"0\" means that it did not appear in the accident, as shown in Table 6; the C1 risk factor appeared in the accident investigation report with serial number 5 and did not appear in the accident investigation report with serial number 4. As shown in Table 6, risk factor C1 appeared in the accident investigation report with serial number 5, while it did not appear in the accident investigation report with serial  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Serial** & **C1** & **C2** & **C3** & **C4** & **C5** & \(\cdots\) & **R2** & **R3** & \begin{tabular}{c} **Accident** \\ **Level** \\ \end{tabular}  \\ \hline
2 & 0 & 1 & 0 & 0 & 1 & \(\cdots\) & 0 & 0 & G \\
3 & 0 & 0 & 0 & 0 & 0 & \(\cdots\) & 1  
number 4. The rightmost column shows the accident level corresponding to each accident, forming a 514 \(\times\) 34-dimensional dataset for association rule mining. Support, Confidence, and Lift are three critical metrics to measure association rules, and strong association rules can be filtered based on these metrics [30]. Assuming that each accident investigation report is regarded as a \"transaction\", denoted as \(D_{n}\), then the set of transactions in this paper \(D\) is expressed as \(\{D_{1},D_{2},D_{3},\ldots,D_{i},\ldots,D_{514}\}\). Assuming that each safety risk factor in the accident investigation report is regarded as a \"project\", denoted by \(F_{i}\), then the project set of this paper \(F\) is denoted as \(\{F_{1},F_{2},F_{3},\ldots,F_{33}\}\). The Support is the ratio of the number of transactions containing both itemsets \(F_{i}\) and \(F_{j}\) to the gross amount of transactions, and is calculated as shown in Equation (10):  
\[Support\left(F_{i},F_{j}\right)=P\left(F_{i}\cup F_{j}\right)=\frac{P\left(F_{i },F_{j}\right)}{P\left(D\right)} \tag{10}\]  
The Confidence is the existence of the itemset \(F_{i}\) in the transaction of the simultaneous existence of the term set \(F_{j}\) of the transaction, which is calculated as shown in Equation (11):  
\[Confidence\left(F_{i}\to F_{j}\right)=P\left(F_{j}\big{|}F_{i}\right)=\frac{P \left(F_{i},F_{j}\right)}{P\left(F_{i}\right)} \tag{11}\]  
The Lift reflects the fact that the association rules in \(F_{i}\) and \(F_{j}\) correlations, and is calculated as shown in Equation (12):  
\[Lift\left(F_{i}\to F_{j}\right)=\frac{P\left(F_{i},F_{j}\right)}{P\left(F_{i} \right)\times P\left(F_{i}\right)}=\frac{P\left(F_{j},F_{i}\right)}{P\left(F_ {j}\right)} \tag{12}\]  
For \(Lift\left(F_{i}\to F_{j}\right)\), its value less than 1 indicates that \(F_{i}\) has little influence on \(F_{j}\); conversely, it indicates that the rule \(F_{i}\to F_{j}\) is of practical significance, suggesting that the appearance of \(F_{i}\) largely leads to the appearance of \(F_{j}\); and its value equal to 1 indicates independence from each other. According to the above principle, to obtain more valuable and higher correlation rules between safety risk factors in chemical enterprises, after several experiments, the minimum Support is set to 5%, the minimum Confidence is set to 20%, and the enhancement threshold is set to 1.2.