Design and Implementation of an AI-Assisted Teacher Performance Evaluation System

To enhance the scientific and objective nature of performance evaluations for university teachers, this study proposes a hybrid evaluation method that combines a fuzzy comprehensive evaluation model with BP neural networks based on a multi-dimensional indicator system. The system adopts a B/S three-tier architecture, achieving efficient data processing through the division of the data layer, business logic layer, and presentation layer. Utilizing technologies such as Spring Boot and MySQL, the evaluation platform is constructed and integrates functional modules including data collection, processing, and result visualization. Pilot run results indicate that the system excels in evaluation efficiency, accuracy, and user satisfaction, particularly demonstrating significant advantages in handling multi-source heterogeneous data and automated evaluations. This system provides a reliable tool for university teacher performance management and promotes the intelligent advancement of teaching management processes.