Early Warning Method of Postgraduate Education Quality in Colleges and Universities Based on Multisource Information Fusion

With the rapid development of postgraduate education in my country, the contradiction between its quantitative growth and quality improvement has become increasingly prominent. At the same time, due to the downward shift in the focus of postgraduate education management, the role of provincial governments in the reform and development of postgraduate education has become increasingly important. Therefore, strengthening the practice of quality supervision and evaluation of postgraduate education at the provincial level is not only a realistic result of the development of postgraduate education in my country but also an inevitable requirement for the further development of postgraduate education in my country in the future, which has an important theoretical and practical significance. With the rapid development and wide application of computer network technology, communication technology, and multisensor technology, multisource information fusion has been selected by many colleges and universities as a method to evaluate the quality of postgraduate study because of its closeness to the frontier of disciplines. Therefore, how to effectively improve the teaching quality of multisource information fusion courses is a practical problem that needs to be solved urgently, and the improvement of teaching quality is also a key factor affecting the comprehensive quality of students. This paper deeply analyzes the setting and requirements of the information fusion course, combines the characteristics of mathematical theory and tools in information fusion, uses multisource data to model student behavior, and proposes a serialized prediction framework based on a deep network. The network structure reasonably integrates multisource data and automatically extracts behavioral characteristics. Combined with the static characteristics of students, the paper uses the long-term and short-term memory network to model the overall online behavior of students and finally predicts the probability that students have academic risks. The model is tested on a real university course dataset, and the experimental results show that the performance of the proposed algorithm is better than the baseline comparison algorithm.