Developing a Clustering Structure with Consideration of Cross-Domain Text Classification based on Deep Sparse Auto-encoder

Yufan Guo, Rong Fei, Kuan Zhang, Yu Tang, Bo Hu

Abstract: feature dimension and cross-domain classification in text classification may lead to low efficiency of text classification. Therefore, in order to study the specificity of the 2019-novel coronavirus, this paper proposes an improved clustering structure based on deep sparse auto-encoder for cross-domain text classification. In this structure, the word vector model and cosine similarity are used to construct the similarity matrix, and then the deep sparse automatic encoder based on unsupervised learning is used to reduce the dimension and extract the feature structure of complex network, and the k-means clustering method is used for testing. Finally, the results are obtained through mean-shift autonomous classification. The performance of the structure is verified on the data set of the title of the paper. The experimental results show the effectiveness of the structure and the practicability of the paper.