为解决传统的橡胶沥青基本性能试验耗时耗力的问题，基于BP神经网络，提出了一种橡胶沥青基本性能的预测方法。通过对大量实验数据的归一化处理分析，采用标准化技术提高模型的训练效率，构建了橡胶沥青的针入度，软化点，延度的人工神经网络（artificial neural network, ANN）预测模型，在模型训练过程中，应用遗传算法进行优化，并使用早停法和学习率调度进行优化，以确保模型的泛化能力。结果表明：通过对测试集的评估，优化后的模型在性能预测上表现出良好效果，均方根误差（root mean square error, RMSE）为0.63，平均绝对误差（mean absolute error, MAE）为0.45，相关系数R达到0.91。该模型能够有效预测橡胶沥青基本性能，提高预测精度，为预测橡胶沥青基本性能提供了可靠参考。

To address the issue of time consumption and labor intensity in traditional testing methods for the basic performance of rubber asphalt, a performance prediction method based on BP neural networks was proposed. A large amount of experimental data was normalized and analyzed, and standardization techniques were utilized to enhance the model's training efficiency. An artificial neural network (ANN) prediction model was constructed for the penetration, softening point, and ductility of rubber asphalt. Genetic algorithms were applied for optimization during the model training process, and early stopping and learning rate scheduling were employed to ensure the model's generalization ability. The results indicate that the evaluation of the test set demonstrates good performance in prediction, with a root mean square error (RMSE) of 0.63, a mean absolute error (MAE) of 0.45, and a correlation coefficient (R) of 0.91. This model effectively predicts the basic properties of rubber asphalt, improves prediction accuracy, and provides a reliable reference for future predictions of rubber asphalt properties.