Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm

Peng Zhang, Zifan Ma, Zeyuan Ren, Hongxiang Wang, Chuankai Zhang, Qing Wan () and Dongxue Sun
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Peng Zhang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Zifan Ma: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Zeyuan Ren: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Hongxiang Wang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Chuankai Zhang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Qing Wan: Undergraduate School, Hebei University of Technology, Tianjin 300401, China
Dongxue Sun: Party Committee Organization Department, Hebei University of Technology, Tianjin 300401, China

Mathematics, 2024, vol. 12, issue 8, 1-21

Abstract: With the continuous deepening of educational reform, a large number of educational policies, programs, and research reports have emerged, bringing a heavy burden of information processing and management to educators. Traditional manual classification and archiving methods are inefficient and susceptible to subjective factors. Therefore, an automated method is needed to quickly and accurately classify and archive documents into their respective categories. Based on this, this paper proposes a design of an automatic document classification system for educational reform based on the Naive Bayes algorithm to address the challenges of document management in the education field. Firstly, the relevant literature and document data in the field of educational reform are collected and organized to establish an annotated dataset for model detection. Secondly, the raw data are preprocessed by cleaning and transforming the original text data to make them more suitable for input into machine learning algorithms. Thirdly, various algorithms are trained and selected to determine the best algorithm for classifying educational reform documents. Finally, based on the determined algorithm, a corresponding classification software is designed to automatically classify and archive educational reform documents for analysis. Through experimental evaluation and result analysis, this research demonstrates the effectiveness and accuracy of the education reform document automatic classification system based on the Naive Bayes algorithm. This method can efficiently classify a large number of documents into their respective categories quickly and accurately, thereby improving the efficiency of educators and their information management capabilities. In the future, further exploration of feature extraction methods and machine learning algorithms can be conducted to optimize the classification performance and apply this method to practical management and decision-making in the education field.

Keywords: educational reform; manual classification; Naive Bayes algorithm; feature extraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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