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Liang, J., Chen, J. H., Zhang, X. Q. et al. (2019). Anomaly Detection Based on One-Hot Encoding and Convolutional Neural Network. Journal of Tsinghua University (Science and Technology), 59, 523-529. (In Chinese)

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• TITLE: Application of Precise Loan Qualification Identification Based on K-Means and Decision Tree Model from the Perspective of Consumer Behavior in Universities

  AUTHORS: Yan Fan, Chaosheng Zhang, Xiao Ge

  KEYWORDS: Targeted Loan Disbursement, Student Loans, Consumer Behavior, K-Means Algorithm, Decision Tree Model

  JOURNAL NAME: Journal of Service Science and Management, Vol.18 No.6, November 28, 2025

  ABSTRACT: From the perspective of student consumption behavior, a data-driven framework for screening student loan eligibility was developed using K-means clustering analysis and decision tree models. A questionnaire survey was conducted on 829 students at colleges and universities to collect comprehensive data covering various dimensions such as economic background and consumption patterns. The K-means algorithm successfully predicted and identified the loan eligibility of the samples, with its predictive performance demonstrated by combining it with the decision tree model. Additionally, through in-depth discussions with credit departments, its practical value and reliability were confirmed. This study has enhanced the data-driven intelligent decision mechanism and provided strong support for precise loan disbursement in student loans, paving the way for the application of financial technology in credit areas.