针对碳纤维增强复合材料剩余刚度预测中传统机器学习模型可解释性差及显式回归模型泛化能力弱的问题，提出一种基于符号回归与反向传播神经网络（BPNN）的可解释性混合建模方法。基于T800碳纤维复合材料静力与疲劳试验数据，构建了包含多应力水平、多载荷类型的刚度退化数据集。结合皮尔逊相关系数、最小冗余最大相关性算法、SHAP值（SHapley Additive explanations）分析筛选出关键特征（应力水平、归一化寿命、材料极限强度）。利用符号回归提取显式物理规律，并通过BPNN捕捉非线性刚度退化。结果表明：符号回归模型在双参数耦合预测精度显著优于传统显式回归模型，成功量化了应力水平对刚度退化的调控作用；BPNN模型在三参数耦合预测中精度更高，且跨载荷类型预测误差可控。该框架通过平衡物理可解释性与非线性建模能力，为复合材料疲劳损伤评估提供了高精度、物理透明的新方法。

The conventional standard regression models often struggle to accurately predict the residual stiffness of Carbon Fiber Reinforced Polymers （CFRP）， while data-driven approaches typically lack interpretability. To address these challenges， we introduce a novel method that integrates Back Propagation Neural Network （BPNN） with Symbolic Regression （SR）. A stiffness degradation dataset is constructed using static and fatigue test data from T800. Key features， including stress level， normalized life， and strength， are selected through methods such as Pearson Correlation Coefficient （PCC）， Max-Relevance and Min-Redundancy （mRMR）， and SHAP analysis. SR is employed to uncover clear physical principles， while BPNN effectively captures complex relationships among multiple parameters. The results indicate that SR significantly outperforms traditional models in predicting the combined effects of stress level and normalized life. Additionally， BPNN demonstrates greater accuracy in predicting the interactions among stress level， normalized life and strength， maintaining low prediction errors across varying conditions. This integrated framework successfully merges physical interpretability with the capacity to model intricate relationships， offering a valuable tool for precise and transparent fatigue damage assessment in composite materials.