为了合理地对沥青路面性能进行综合评价，针对传统模型的不足，提出了支持向量机 (SVM) 路面性能综合评价模型、沥青路面性能训练集及训练标签的确定方法。同时，分别采用交叉验证 (CV)、粒子群算法 (PSO)、遗传算法 (GA) 3 种优化模型对影响模型精度关键的惩罚参数 C 与核函数参数 g 进行寻优，其准确率分别为99. 60%，96. 67%，94. 77%，可见交叉验证寻优所得到的最佳参数分类精确率最高。最后以广东省某高速公路 23 个养护路段为例，分别使用支持向量机模型与 《公路技术
状况评定标准》对路面性能进行综合评价。结果表明，使用本模型所得到的评价结果更符合实际。

Focusing on the shortcomings of traditional models，a pavement performance evaluation model based on support vector machine (SVM) was proposed to comprehensively evaluate the performance of asphalt pavement．The method to determine the asphalt pavement performance training set and training labels was also proposed． At the same time，three optimization models，namely cross-validation (CV)，particle swarm optimization (PSO)，and genetic algorithm (GA)，were used to optimize the penalty parameter C and the kernel function parameter g which have effects on the accuracy of models，and their accuracy rate can reach to 99. 60%，96. 67%，94. 77% respec-tively． The results show that the best parameters obtained by cross-validation optimization have the highest accura-cy． Finally，taking the 23 maintenance sections of Guangyun Expressway as an example，the support vector ma-chine model and Technical Evaluation Standard for Highway Technology are respectively used to evaluate compre-hensive performance of the pavement． The results show that the model proposed in this paper is more suitable for practical application．