在道路施工及养护过程中，高效、准确地测量沥青混合料中的集料级配是保证混合料骨架结构稳定及施工质量的重要环节。针对基于单一几何模型应用在粗集料颗粒分档时，存在粒径计算不准确、无法满足施工要求的问题，文中提出一种基于多特征因子的路用集料粒径计算神经网络模型，实现对集料颗粒粒径的准确计算。首先，对采集到的集料颗粒图像进行几何特征提取，并对提取到的特征数据进行数据清洗和归一化等处理，建立样本数据集; 然后通过相关性分析，提取出与集料粒径相关性较强的特征因子; 最后，构建多层感知机 (MLP) 神经网络模型对数据集进行训练，并采用敏感性分析得到用于表征集料粒径的重要特征权重，实现对集料粒径的准确计算。结果表明，文中提出的集料粒径计算方法与卡尺法测量的结果拟合精度较高 (相关系数R² =0. 91)，与二阶矩、等效椭圆等传统几何模型方法相比不仅明显提高了精度，而且可以实现快速虚拟筛分，显著提升后续的筛分效率。

In the process of road construction and maintenance，the efficient and accurate measurement of aggregate gradation in asphalt mixture is an important factor to ensure the stability of mixture skeleton structure and construc-tion quality． Considering the methods based on a single geometric model can not meet the requirements of particle size calculation accuracy in construction practice，a neural network model for road aggregate size calculation based on multiple features was proposed． Firstly，geometric features were extracted from the collected aggregate particle images，and the extracted feature data were cleaned and normalized to establish the sample data set． Secondly，the characteristic factors with strong correlation with aggregate particle size were extracted by correlation analysis． Fi-nally，multi-layer perceptron (MLP) neural network was constructed to train the data set，and the important cha-racteristics weight representing aggregate particle size was obtained by the sensitivity analysis． Thus the particle size of coarse aggregate can be accurately calculated． The results show that the aggregate particle size calculation meth-od proposed in this paper has a higher fitting accuracy (R² =0. 91) than the results measured by the traditional geo-metric models such as secondary moment and equivalent ellipse． It not only improves the accuracy obviously，but also realizes fast virtual screening and significantly improves the subsequent screening efficiency．