Corrosion images are the most accessible type of data to collect. To utilize these data to monitor and evaluate the corrosion of weathering steel (WS) efficiently. This study established a dataset of corrosion images of WS by accelerated corrosion experiments with dry and wet cycles. Image segmentation algorithms and machine learning were used for training, modeling, and data mining of the image data. The results showed that 22 features in the corrosion images were highly correlated with the corrosion thickness loss, with the highest correlation coefficient exceeding 0.7. The rust layer texture and color features showed a synergistic evolution during the corrosion process. On this basis, a classification model of the rust layer was developed to quantify the evolution of the protective properties of the rust layer during the corrosion process. In addition, the established integrated learning model achieves accurate prediction (R2 > 0.94) for WS corrosion thickness loss.