# Chinese News Text Classification Method via Key Feature Enhancement
## 2 Related Work  
Text classification has a long history in the field of natural language processing (NLP) [3]. The early research was mainly based on expert rules from the 1980s. It was developed to use knowledge engineering to build an expert system to realize the text classification. Until the early 21 st century, with the explosive growth of the number of internet texts and the maturity of machine learning theory, a set of solutions to large-scale text classification problems, based on feature engineering combined with classification models, was gradually formed. Feature engineering plays an extremely important role in machine learning methods. Generally speaking, a machine learning problem is the process of converting data into information and then refining it into knowledge. The role of feature engineering [4] is mainly to extract information from data, while the role of the classifier is to transform the extracted information into knowledge. Feature engineering mainly covers three parts: text preprocessing, feature extraction and feature representation. The ultimate goal is to convert text into a computer-understandable vector form. The traditional text representation method is mainly represented by the bag of words [5] or the vector space model [6]. The disadvantages of these methods are that the text context is ignored and the features are independent of each other. In order to improve the above shortcomings, [PERSON] [7] proposed a distributed representation theory of features, in which the main idea is to express each word as an n-dimensional dense real number vector. In the early stage, the one-hot vector [8] was mainly used for representation. With the development of technology, a series of excellent distributed representation technologies such as word2 vec [9], Glove [10], BERT [11] and GPT-X [12] were successively proposed, which greatly promoted the development of feature representation technology.  
The current mainstream classification models are mainly divided into two categories: traditional statistical learning methods and deep learning methods. The most common statistical learning models for text classification tasks are Support Vector Machine (SVM) [13] and Naive Bayesian (NB) [14] models. The advantage of this type of model is that it is simple and effective, but the disadvantage is that it needs to use manual feature engineering to extract feature vectors, which is time-consuming and has poor scalability. With the development of deep learning technology, scholars have proposed advanced classification models such as FastText [15], CNN [16], RNN [17] and their series of variants and have achieved good classification results. The advantage of the deep learning model is that it can accurately predict the text category by combining the distributed word vector representation technology with the deep neural network. However, the disadvantage is that large-scale labeled text datasets need to be manually annotated to train high-quality classifiers. In order to improve the model, the literature [18; 19; 20] has proposed a variety of effective data enhancement methods to improve the performance of text classification through content compression or content expansion.  
One must comprehensively consider the huge advantages of feature engineering and deep learning methods as well as data enhancement techniques in text classification tasks. This paper proposes a key feature-enhanced Chinese news text classification method through in-depth study of the advantages of the three major technologies. It can extract key features based on part of the original annotation data and complete semantic extension to achieve data enhancement to automatically expand the annotation samples. Finally, combination with deep convolutional neural network model efficiently realizes the automatic classification of news texts.