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In order to realize the intelligent evaluation of carbon steel graphitization, an automatic classification model of carbon steel metallographic images is constructed based on convolutional neural network and transfer learning. The carbon steel graphitization image dataset was firstly established by the data enhancement methods of geometric transformation and pixel adjustment. Then the lightweight EfficientNet network that uniformly expands the network width, depth and resolution to coordinate accuracy and efficiency was used as the backbone feature extraction network to construct a carbon steel graphitization image evaluation model, and transfer learning and parameter fine-tuning methods were used in the training phase to improve the training efficiency of the model. Finally, a test data set was used to verify the classification accuracy and complexity of the model. The results show that the model can quickly and accurately grade the degree of graphitization of carbon steel automatically. With only 12 MB of memory, it can achieve a 97.01% accuracy, and an average detection time of only 10.27 ms for a single metallographic image, which meets the accuracy and real-time requirements of on-site detection.