Research on Intelligent Early Warning of University Budget Expenditure Based on K-Means Clustering Algorithm

With the advent of the era of big data and artificial intelligence, the internal management of colleges and universities is also constantly advancing from the traditional management mode to the intelligent management. A large amount of financial and business data accumulated by the financial cloud system can be used to realize intelligent early warning of financial expenses, which helps financial managers and financial personnel timely discover the dangers existing in the expenditure control of the unit, and is of great significance for further strengthening the expenditure control of colleges and universities and promoting the informatization construction of internal control. This paper uses the method of machine learning to design an intelligent early warning framework for budget expenditure control in colleges and universities, and uses the K-means clustering algorithm to design an early warning method for the integrity control of college expenditure approval process. The early warning data is classified into unsupervised clusters and early warning levels of various data to achieve early warning for the integrity control of college expenditure approval process.