## 1 Introduction  
After the enterprise business is fully informationized, each terminal stores a large amount of text data, making it difficult for administrators to determine which text contains sensitive information, and it is impossible to apply control measures to all the text containing sensitive information. Some documents that do not have obvious confidential marks can easily evade regulatory measures and circulate freely inside and outside the company, ultimately leading to the leak of secrets. At present, most of the information security solutions still use firewalls [1], intrusion detection [2], network antivirus approaches [3], and other methods, which are relatively general and rough, lack the judgment and detection of information content, and cannot refine the management and control means. Data leakage prevention (DLP) [4] is the use of technical measures to prevent data assets from leaking out of an enterprise in violation of security policies. The classification and control of all documents in the terminal and the setting of protection measures can be a good way to block this leak vulnerability. The current data leakage protection methods mainly include internal data encryption [5], identity authentication [6], and data flow control [7]. The information leak prevention solution based on text classification [8] scans and identifiesall outgoing texts sent by terminals in real time; identifies the category of the text in time; checks whether the text contains sensitive keywords [9] and the secret level of the text, which can be used to detect and deal with the leak phenomenon in real time; and then deals with the text through relevant preset means, such as a warning, alarm, or blocking. Therefore, this results in higher performance requirements for real-time massive text classification algorithms. Text classification means sorting a target text into a given category, and is widely used in sentiment analysis (SA) [10], news classification (NC) [11], natural language inference (NLI) [12], and other fields. It is a basic step to construct a text classification model by learning the categorical feature information of classified text data and then automatically classifying unclassified text. There are already many types of text classification methods, such as the decision tree (DT) algorithm [13], the naive Bayes (NB) algorithm [14], the support vector machine (SVM) algorithm [15], the k-nearest neighbor (kNN) algorithm [16], and classification algorithms based on deep learning [17]. The DT algorithm establishes a mapping between object attributes and object values, and classifies unlabeled texts by constructing decision trees. The basic idea of NB classification is to estimate the category probability of a given text using the joint probability of the phrase and category. SVM is a popular supervised learning algorithm. [PERSON] et al. selected a group of informative samples using the posterior probabilities provided by multi-class SVM classifiers to enhance the classification accuracy [18]. [PERSON] et al. investigated the performance of the three classification algorithms--kNN, DT, and NB--as classifiers on Saudi Press Agency datasets [19]. Classification models based on deep learning mainly include the convolution neural network (CNN) [20], the recurrent neural network (RNN) [21], and a combination of other related models. [PERSON] et al. used a direct citation network with cluster analysis in a hybrid bibliometric approach to depict the historiographic development of the technology-enhanced learning (TEL) research domain in higher education [22]. A comparison of the above typical algorithms is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Method Used** & **Datasets** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{DT [13]} & \multirow{4}{*}{Explore emotion classification with DT} & \multicolumn{1}{c}{ \begin{tabular}{c} Composed of 2924 news \\ articles of April 2007 and \\ February 2008 from Sina \\ \end{tabular}  } & \multirow{4}{*}{\