Evaluation of Mathematics Teaching Quality Using the PSO-BPNN Algorithm

With the rapid advancement of artificial intelligence and computational intelligence, traditional evaluation methods struggle with issues such as high subjectivity, low efficiency, and insufficient accuracy. To enhance evaluation performance, this paper introduces a novel approach integrating Particle Swarm Optimization (PSO) with a Backpropagation Neural Network (BPNN), where PSO optimizes BPNN weights and biases to enhance convergence speed and prediction accuracy. The optimized BPNN model is then applied to assess various quantitative indicators, ensuring a more objective and efficient evaluation process. Experimental results demonstrate that the PSO-BPNN algorithm outperforms conventional methods in terms of accuracy and stability. This study highlights the potential of intelligent algorithms in automated evaluation tasks, providing a robust computational framework for data-driven decisionmaking in performance assessment.