Next Article in Journal
Privacy and Security Issues in Online Social Networks
Next Article in Special Issue
Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
Previous Article in Journal
Query Recommendation Using Hybrid Query Relevance
Previous Article in Special Issue
Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessEssay
Future Internet 2018, 10(11), 113;

Chinese Text Classification Model Based on Deep Learning

School of Computer Science and Technology, Donghua University, Shanghai 201620, China
Author to whom correspondence should be addressed.
Received: 18 October 2018 / Revised: 12 November 2018 / Accepted: 12 November 2018 / Published: 20 November 2018
(This article belongs to the Special Issue Innovative Topologies and Algorithms for Neural Networks)
Full-Text   |   PDF [491 KB, uploaded 22 November 2018]   |  


Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts. View Full-Text
Keywords: Chinese text classification; long short-term memory; convolutional neural network Chinese text classification; long short-term memory; convolutional neural network

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Li, Y.; Wang, X.; Xu, P. Chinese Text Classification Model Based on Deep Learning. Future Internet 2018, 10, 113.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top