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An Optimized Chinese Filtering Model Using Value Scale Extended Text Vector

Siyu Lu1, Ligao Cai1, Zhixin Liu2, Shan Liu1, Bo Yang1, Lirong Yin3, Mingzhe Liu4, Wenfeng Zheng1,*

1 School of Automation, University of Electronic Science and Technology of China, Chengdu, 610054, China
2 School of Life Science, Shaoxing University, Shaoxing, 312000, China
3 Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, 70803, USA
4 College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China

* Corresponding Author: Wenfeng Zheng. Email:

Computer Systems Science and Engineering 2023, 47(2), 1881-1899.


With the development of Internet technology, the explosive growth of Internet information presentation has led to difficulty in filtering effective information. Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering, especially for Chinese texts. This paper selected the manually calibrated Douban movie website comment data for research. First, a text filtering model based on the BP neural network has been built; Second, based on the Term Frequency-Inverse Document Frequency (TF-IDF) vector space model and the doc2vec method, the text word frequency vector and the text semantic vector were obtained respectively, and the text word frequency vector was linearly reduced by the Principal Component Analysis (PCA) method. Third, the text word frequency vector after dimensionality reduction and the text semantic vector were combined, add the text value degree, and the text synthesis vector was constructed. Experiments show that the model combined with text word frequency vector degree after dimensionality reduction, text semantic vector, and text value has reached the highest accuracy of 84.67%.


Cite This Article

S. Lu, L. Cai, Z. Liu, S. Liu, B. Yang et al., "An optimized chinese filtering model using value scale extended text vector," Computer Systems Science and Engineering, vol. 47, no.2, pp. 1881–1899, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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