Teaching Quality Evaluation and Analysis System Based on AHP-PSO-BP Neural Network

Teaching quality evaluation as a way of observing and managing teaching has a long history. The data of students' grading of courses and teachers is the data basis of the whole method. According to the evaluation data, the colleges and universities can objectively understand the teaching quality of each teacher and course. Therefore, more and more attention has been paid to the evaluation of teaching quality. The traditional evaluation method assumes that all evaluation indexes play the same role in the process of comprehensive evaluation. Although this method can easily process students' teaching quality evaluation data and generally reflect the overall level of teaching quality, it has obvious shortcomings in the targeted and detailed performance of teaching quality. In addition, how to calculate the teaching evaluation results efficiently and accurately is also the key issue of teaching quality evaluation research. Therefore, this paper studies the Analytic Hierarchy Process (AHP) and Particle Swarm Optimization Back Propagation (PSO-BP) neural network algorithm, and designs and develops a teaching quality evaluation and analysis system based on the algorithm. The system is developed with Django framework and HTML5 standard. It can effectively and conveniently evaluate the teaching quality, and provide references for colleges and universities to improve the teaching quality.