## 3 Methodology
### Structured Data Classification Framework
#### 3.2.4 Data Classification and Grading Module  
In this module, we present the fundamental principle of quantifying information quantity. Specifically, in structured data, when column A and column B have an equal number of rows and pertain to the same type of sensitive data, the difference \(\in\) in the information they provide falls within a certain range [41]. This can be formulated as follows:  
\[|H(A)-H(B)|\leqslant\in \tag{5}\]  
Here, we introduce \(H(*)\) as a measure function to quantify the amount of information provided by each column of data. \[H(X)=-\sum_{x\in A}P(x)\log P(x) \tag{6}\]  
In Equation (6), \(0\leq P(x)\leq 1\), \(\sum_{x\in A}P(x)=1\) and \(P(x)\) represent the probability of occurrence for each discrete piece of information. To apply the basic principle of information quantity quantization, we computed the average amount of information provided by each subclass \(\{S_{1},S_{2},\ldots,S_{n}\}\) in the data dictionary. This was achieved by defining the equation as follows:  
\[S_{i}={k_{ij}}_{i=1}^{m_{1}} \tag{7}\]  
where \(k_{ij}\) represents a single keyword and \(m_{i}\) (where \(m_{i}\geq 1\)) denotes the number of keywords in subclass \(S_{i}\). To proceed, we identified columns in the dataset with \(k_{ij}\) as the field name and extracted 100 rows from each column to form \(c_{ijkk=1}^{r_{i}}\). Subsequently, we calculated the number of discrete information elements \(q\) and information entropy \(H\) in each class. \[q_{i}=\frac{1}{m_{i}}\sum_{j=1}^{m_{i}}\Biggl{(}\frac{1}{r_{i}}\sum_{k=1}^{r_ {i}}|c_{ijk}|\Biggr{)} \tag{8}\]  
\[H_{i}=\frac{1}{m_{i}}\sum_{j=1}^{m_{i}}\Biggl{(}\frac{1}{r_{i}}\sum_{k=1}^{r_ {i}}H(c_{ijk})\Biggr{)} \tag{9}\]The process of automatically classifying and grading structured data based on synonym discrimination and information quantification, as illustrated in Figure 8, involves the following steps:  
1. Parsing: Structure the data into two parts: field names and data content;
2. Field name identification: Utilize the keywords from the financial data hierarchical classification library to match the field names. If a corresponding field name is found, proceed to step 6; otherwise, move to step 3;
3. Candidate entity selection: Randomly select 100 rows of data (denoted as \(d_{i}\) for \(i=1,2,\ldots,100\)) and identify the data types, such as numeric value, English character, Chinese character, mixed character and date. Consider entities with the same data type from the Financial Data Hierarchical Classification Library as candidate entities;
4. Synonym probability calculation: Apply the synonym discriminant model to determine if the field name is synonymous with the keyword set of the candidate entity. Input the candidate entity's keyword set \(S_{i}\) and the field name s into the synonym discriminant model, resulting in the probability \(P(s\in S_{i})\) that the field name belongs to the keyword set. Iteratively traverse all candidate entities to obtain: \[\{P(s\in S_{1}),P(s\in S_{2}),\ldots,P(s\in S_{i})\}\] (10)
5. Synonym score calculation: Calculate the number of discrete information q and information entropy \(H(d)\) of the data: \[Score=\theta_{1}P(s\in S_{i})-\theta_{2}|q-q_{i}|-\theta_{3}|H(d)-H_{i}|\] (11) \[\theta_{1}+\theta_{2}+\theta_{3}=1\] (12) where \(\theta_{i}\) represents the weight share of each index. The entity belonging to the keyword set with the highest score becomes the classification result, and step (6) is executed;
6. Calibration: Perform stratified sampling of the corresponding content of the field name. Apply sensitive rules belonging to the keywords that match successfully in the feature library under its regular items and dictionaries for secondary detection of the sampling results. Recognition is considered successful if the matching rate exceeds the set threshold; otherwise, it is considered a recognition failure;
7. Output: Output the corresponding entity name and sensitivity level from the Financial Data Hierarchical Classification Library if the recognition is successful. If the recognition fails, output NULL.