An application scheme based on the depth-first search algorithm is presented for the quality monitoring and management system in colleges and universities, considering the limitations of the classic optimization method in this context. To begin, the quality monitoring management system makes use of the depth-first search algorithm to build the application scheme after precisely locating the influencing elements using the depth-first traversal theory and properly dividing the indications to minimize interference. Based on the experimental findings, it is clear that the suggested scheme has clear benefits over the usual optimization method when it comes to processing speed and application accuracy of parameters affecting the quality monitoring and management system. The use of a quality monitoring and management system is crucial in higher education since it allows for the precise prediction and optimization of growth traits and product production in higher education. When it comes to handling the simulation issue of quality monitoring and management, however, the classic optimization technique has its limits, particularly when dealing with complicated situations. In order to address this issue more effectively, this research suggests a depth-first search algorithm-based application scheme for quality monitoring management systems. In order to establish the division of indicators, the method employs the depth-first search algorithm and precisely locates the influencing components using the depth-first traversal theory. The scheme’s accuracy and speed are much enhanced for various challenges, and it has superior performance, according to experimental findings under certain evaluation criteria. Consequently, the depth-first search algorithm-based simulation scheme can enhance the efficiency and accuracy of simulations used in college and university teaching quality monitoring and management systems, while also better addressing the limitations of traditional optimization algorithms.