In order to improve the efficiency and accuracy of corroded cracks detection and classification in rein? forced concrete, a corroded cracks identification model Steel Corrosion Net (SCNet) based on deep learning Convolu? tional Neural Network (CNN) is proposed. A data set of 39 000 crack figures is firstly built by original data collection and data enhancement, a SCNet three-classification neural network model is then built and tested by TensorFlow learning framework and Python. According to the training and testing accuracies of the model, the structure and pa? rameters of the SCNet network model are optimized and the result of the SCNet is compared with two traditional test? ing methods. The result shows that the SCNet model established in this paper achieves the classification accuracy of 96.8%, which means the SCNet model can effectively identify and classify the corroded cracks in reinforced concrete with high accuracy and measurability. Under the conditions of noise interference such as shadows and distortions, those two traditional testing methods fail to ideally classify, whereas the SCNet model shows a relatively stable classification performance.