Evaluating the quality of ideological and political education (IPE) presents unique challenges due to its inherent subjectivity, semantic ambiguity, and the dynamic nature of student perception. Traditional assessment methods—reliant on fixed rubrics or periodic surveys—fail to capture the nuanced cognitive and emotional feedback patterns essential to political-ideological teaching. To address these limitations, this paper proposes a volatility-aware, fuzzy comprehensive evaluation framework tailored to the complexities of IPE. By integrating multi-level fuzzy membership functions, adaptive feedback sampling models, and role-sensitive evaluation indicators, the proposed system models teaching quality as an evolving semantic decision space. We simulate various feedback introduction scenarios (single, monthly, weekly, daily) under different fuzzy volatility coefficients and semantic boundary configurations, analyzing the resulting system anomalies, stability trends, and semantic drift. Our findings reveal that excessive sampling can lead to feedback-induced volatility, while role-based weighting schemes significantly enhance robustness and interpretability. Additionally, control strategies such as cohort-based sampling and semiweekly feedback collection were shown to mitigate instability in high-volatility settings. Experimental results, visualized in multiple figures, demonstrate that the proposed model outperforms traditional fuzzy or rule-based approaches in terms of anomaly suppression, semantic adaptability, and feedback governance.
