Research and Application of C4.5 Combined with Naive Bayes in the Identification of Poverty among University Students

Higher education occupies an important position in China's education system, but the high poverty rate results in a heavy workload for poverty identification. The current poverty identification process mainly relies on manual review of documents, with low levels of scientific quantification of data. To address the shortcomings of the Naive Bayes algorithm's inability to capture complex relationships between feature models, preprocessing with the C4.5 algorithm is employed. Firstly, the K-means algorithm is used to discretize transaction amounts. Secondly, the C4.5 algorithm is utilized to construct a decision tree model and extract key features. Finally, the Naive Bayes algorithm is applied for classification using the attributes selected by the C4.5 algorithm.