## 2 Materials and Methods
### Feature Selection
When feature selection algorithms _tf-DE_, _tf-idf_, _tf-OR_, and _tf-rf_ are used for comparative testing, the test results show that as the number of feature words increases, the classification effect of the text classification algorithm will increase, as shown in Figure 5. In the _tf-DE_ algorithm, when the feature vector dimension is increased from 1000 to 15,000, _F1-measure_ also increases from 86.3% to 93.5%. When the feature vector dimension is small, the _tf-DE_ algorithm can hardly reflect the advantages. We believe that this is due to the diversity of the Chinese vocabulary; this makes it difficult to gather a certain number of keywords representing the features of documents, so Chinese text needs more feature words to judge its category. Therefore, our algorithm can only reflect its advantages when the number \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Indicator** & \multicolumn{2}{c}{**Accuracy \(P\)**} & \multicolumn{2}{c}{**Recall \(R\)**} & \multicolumn{2}{c}{**F1-Measure**} \\ \cline{2-7}  & **kNN** & **SVM** & **kNN** & **SVM** & **kNN** & **SVM** \\ \hline finance & 95.7\% & 91.1\% & 88.3\% & 98.0\% & 91.8\% & 94.4\% \\ education & 87.6\% & 95.3\% & 85.7\% & 73.5\% & 86.6\% & 83.0\% \\ technology & 82.7\% & 94.8\% & 90.8\% & 85.0\% & 86.6\% & 89.6\% \\ sports & 96.1\% & 83.9\% & 95.8\% & 99.5\% & 96.0\% & 91.0\% \\ entertainment & 95.2\% & 94.7\% & 97.1\% & 99.5\% & 96.2\% & 97.0\% \\ average & 91.5\% & 92.0\% & 91.5\% & 91.1\% & 91.4\% & 91.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multi-classification effects of kNN and SVM. Figure 4: Test results of text classification algorithm under _tf-DE_. ## 5 Conclusions and Future Work

This paper describes a method for optimizing the automatic classification of sensitive electronic documents in enterprises and organizations to provide different levels of disclosure risk assessment by setting a unified inspection strategy. The proposed approach utilizes a combination of keyword retrieval and the kNN classification algorithm to standardize and enhance confidentiality management. In view of the circumstances, namely that the existing feature selection algorithm _tf-idf_ ignores the distribution difference of words among and within different categories, and existing text classification algorithms based on kNN have high time and space complexity and large storage resource consumption, an optimization method is proposed to improve the performance of the feature selection algorithm and the kNN algorithm using AVX-256 to achieve real-time classification of massive texts. The features of the text are described and stored by vectors, and the operations that need to be performed separately by data are replaced by one vector operation. Firstly, the keyword dictionary and text feature vector are described in vectors, and the data vector is constructed, analyzed, and operated via SIMD instruction. Secondly, the weight operation and distance operation of multiple data are simplified into a vector operation to reduce the consumption of the CPU's computing resources. The algorithm with AVX-256 acceleration proposed in this paper improved the efficiency of high-dimensional feature vector computation performance, and the obtained classification results are comparable with those of the traditional classification method, SVM. Although the kNN algorithm is simple and efficient, its accuracy needs to be improved. The deep learning models do not rely on artificially acquired text features, and can directly learn and model the text content, which is a great advance compared with the shallow learning models. For example, an RNN can recursively learn text semantic and syntactic tree structures without the need to set artificial features. A CNN can simultaneously use different convolution kernels for convolution operations on text sequences. There are also many studies that combine multiple deep learning models. In the follow-up work, the deep learning model for real-time text classification will be studied to improve multi-classification.