Road cracks are the key element of road damage, the classification of which can be used to arrange the formulation of road maintenance strategy in targeted ways. As the classification with manual annotation is time-consuming and inefficient, this study proposes a road-crack image classification method based on contrastive learning. In the traditional contrastive learning framework, the feature extraction part is improved to make the model more sensitive to the features of small cracks. Firstly, the data is augmented; the ResNet50 part is improved in feature extraction, and the multi-scale method is used to extract features. Then, multilayer perception (MLP) is employed to reduce the dimensions of extracted features and project them onto vector space. Finally, cosine similarity and cross-entropy loss of normalized temperature scale are applied to optimize the model. The experimental results reveal that compared with the original model, the improved model has a classification effect of 92.1% on crack images, an increase of 0.22%, which indicates it has a good effect on crack image classification.