Intelligent Evaluation Framework for Ideological and Political Education Leveraging Optimized BP Neural Networks

Ideological and political education plays a crucial role in shaping the values, perspectives, and civic responsibility of students, yet traditional evaluation models often rely on subjective judgment and rigid assessment criteria, leading to low accuracy and inconsistency in results. Existing approaches such as standard machine learning classifiers and conventional networks have shown promise but still suffer from limitations such as slow convergence, sensitivity to initial parameters, and local optima trapping, which reduce their reliability in large-scale educational evaluations. To address these drawbacks, this paper proposes an improved evaluation model of ideological and political education using a BP neural network optimized by the Tunicate Swarm Optimization (TSO) algorithm. The TSO enhances the learning efficiency of BP by adaptively adjusting weights and biases, thereby improving generalization and convergence speed. Experimental validation was conducted on the Students Performance Dataset from Kaggle, achieving an accuracy of 98.5 %, precision of 98.2 %, recall of 97.9 %, specificity of 98.3 %, and F1-score of 98.0 %. Comparative analysis with existing algorithms such as Random Forest, Support Vector Machines, and standard BP networks demonstrates that the proposed BP-TSO model consistently outperforms them across all evaluation metrics. The results indicate that our model provides a more objective, accurate, and scalable framework for evaluating ideological and political education.