Evaluation of Chinese Language Teaching Quality Based on an IPSO-BP Neural Network Algorithm

The evaluation of Chinese language teaching quality plays a critical role in enhancing educational outcomes and improving teaching effectiveness. Traditional methods of evaluation often rely on subjective criteria and limited data, leading to inaccuracies and a lack of adaptability. This study proposes an improved BP (Back Propagation) neural network algorithm to evaluate Chinese language teaching quality. The methodology integrates three key computational stages: initial data normalization, discriminative feature selection, and neural network refinement through microbial optimization techniques. This comprehensive processing pipeline substantially improves both the training efficiency and predictive performance of the assessment framework. Comparative analyses reveal the superior capability of the proposed system over conventional evaluation approaches, particularly in maintaining robust accuracy across diverse instructional contexts. These advancements offer meaningful contributions toward establishing more reliable and scalable quality assessment mechanisms for digital education, yielding practical implications for both pedagogical improvement and institutional decision-making.