Study on Logistic Service Management of Colleges and Universities Based on Data Mining Algorithms

Abstract

Construction of a large logistics service (LS) that can adapt to the new situation is necessary for improving the self-development capability of university logistics in the reform process of socialization, and the measures are as follows: with support from the government sector, to create an external environment; with resource integration as a goal, to create an organizational structure; with market mechanism as a promoter, to the Independent college is a significant innovation of the higher education system, whose method of operation achieves the partnership between resources and social forces in higher education. There are several references in the text for further logistic reform in universities via data mining (DM) algorithms concerning logistic entities and autonomous colleges, which examine the market features and interaction between them. The logistics service data mining (LS-DM) approach plays a critical role in advancing logistic management science while boosting the economy's overall benefits when used with other measures. As a result of the rapid popularization of higher education, new features and models place an even greater demand on logistics management in colleges and universities. Refined management must be advocated and implemented in the new scenario. To apply refined management, you must alter your management philosophy, fine-tune your rules and regulations, enhance performance capabilities, and put mechanisms for monitoring and assessing progress. As a result, logistics management can be continuously improved, students and teachers receive better and more gratifying services, and the scientific growth of colleges and universities may be laid solidly.. The proposed LS-DM system with logistics service, data mining, and machine learning model demonstrates simulation outcomes with an accuracy of 89.7% and a precision of 87.8%, which is greater than the accuracy and precision exhibited by the existing models.