## 1 Introduction  
With the rapid increase of communication on the internet, a large amount of textual data has been generated. How to mine important and useful information from these textual data is of great significance to promote the development of various industries [1, 2]. Text classification is one of the important information retrieval and data mining technologies, the purpose of text classification is to divide a given text into one or multiple predefined categories according to the extracted features [3]. It is an important research direction in natural language processing, and has been widely used in various applications, such as information retrieval, document classification, sentiment analysis and so on [4]. Among them, short text classification has attracted more attention and encountered more challenges due to their limited length. Most of the internet data are short texts, such as microblogs and bulletin board system (BBS) [5]. Accurate short text classification plays an important role both for the enterprises and individuals, even for the government services. It can help the enterprises to understand public preferences in a better way, thus to quickly grasp business opportunities, and increase revenue. For the users, it is also helpful for solving the problem of network information chaos, and facilitate users to hunt information and get the useful information needed. For the government services, it assiststhe government to understand public opinions and provide better services [6]. With the explosively increasing short texts on the Internet, how to efficiently generate practical value from these massive short texts is an urgent problem to be solved [7]. In the past decades, scholars have made various attempts to improve the accuracy of short text classification [8]. Many efforts have been made in two aspects: text representation and classifier. Text representation is the basis of text classification system, and its modeling quality determines the performance of the classification system. Since the traditional text representation methods model words according to the word frequency, when applied to represent words characterized by high dimension and high sparsity, it would result in two difficulties: one is that the semantic features extracted are insufficient; the other is that it is difficult to support the short text classification task with increasing data volume since it mainly depends on human beings to extract features [9]. Currently, the distributed representation of words is a popular method in the field of text presentation. Its principle is to map words to fixed dense data vectors, namely Word Embedding [10]. Compared with traditional one-hot Encoding [11; 12], bag-of-word model [13], and vector space model [14; 15], etc., the distributed representation possesses relatively good semantic feature expression competence, and in the meantime data form can be read and processed efficiently by neural networks. In recent years, word vector is widely popular in the field of text semantic modeling, which can be attributed to Google's open-source Word2 vec word vector tool [16]. Upon its launch, research on text classification model has been transformed from the conventional shallow machine learning models to the deep learning models. By means of vector in mathematics, words are able to map the text input to a low dimensional, continuous and dense vector matrix, so that deep neural networks can be transferred to text categorization, which greatly promotes the development of related researches  
In terms of classifiers, there are also various methods that have been put forward, such as NB [17; 18; 19], K-Nearest Neighbor (KNN) [20; 21; 22], SVM [23] and so on. For instance, [PERSON] et al. improved the performance of NB in terms of text classification by using two heuristic methods: text normalization and feature weights [20]. [PERSON] et al. [21] proposed a KNN model-based classifier named KNN model, which combines the advantages of both KNN and Rocchio. [PERSON] et al.