  In the era of big data, a large number of text data generated by the Internet
has given birth to a variety of text representation methods. In natural
language processing (NLP), text representation transforms text into vectors
that can be processed by computer without losing the original semantic
information. However, these methods are difficult to effectively extract the
semantic features among words and distinguish polysemy in language. Therefore,
a text feature representation model based on convolutional neural network (CNN)
and variational autoencoder (VAE) is proposed to extract the text features and
apply the obtained text feature representation on the text classification
tasks. CNN is used to extract the features of text vector to get the semantics
among words and VAE is introduced to make the text feature space more
consistent with Gaussian distribution. In addition, the output of the improved
word2vec model is employed as the input of the proposed model to distinguish
different meanings of the same word in different contexts. The experimental
results show that the proposed model outperforms in k-nearest neighbor (KNN),
random forest (RF) and support vector machine (SVM) classification algorithms.
