Research on Intelligent Decision Support System in Information Technology Service Platform of Colleges and Universities

This paper proposes a solution for information technology service platform of colleges and universities based on intelligent decision support system, which uses text classification and sentiment analysis algorithms to support the platform's intelligent response to the needs of teachers and students and management dynamics. The platform optimizes the text classification and sentiment analysis algorithms of deep learning models to meet the complex needs of information management in colleges and universities, so that it can more accurately identify and respond to various needs of teachers and students and management dynamics within the school. By analyzing data from various information sources such as feedback from teachers and students, opinions and suggestions, and management announcements, the platform can automatically classify and analyze information content in real time, identify the emotional tendencies and demand categories of teachers and students, and help management quickly grasp the overall dynamics when the amount of information is large. To ensure the applicability and efficiency of the system, this paper uses a large-scale data set for experimental verification. The simulation results show that the system performs well in the accuracy of data analysis, with an accuracy rate of more than 92%, and can meet the diverse management needs of colleges and universities in practical applications. At the same time, the response speed of the system has been optimized, and it can quickly analyze a large amount of data, reducing the workload of traditional manual statistics and classification, and greatly improving management efficiency.