In order to identify three types of carbon structural steels whose metallographic structures are ferrite and pearlite. This paper proposes a metal identification method based on convolutional neural network. Convolutional neural networks can efficiently implement classification with complex environmental information, ambiguous inference rules, and flawed samples. The metal identification platform was built based on eddy current non-destructive testing technology and convolutional neural network. First, 8 high-frequency points are randomly selected from the bandwidth of the eddy current sensor, and the metal information that under each frequency point is separately collected by this eddy current sensor. Then, this information is imaged through data processing such as Fourier transform and coordinate transformation. Finally, the identification model is obtained by convolutional neural network. The results show that the proposed scheme can identify metals without damaging the metal compared to the traditional method. The accuracy of the CNN model for all three metals increased to 92. 33%, which is superior to the BP neural network (86. 20%).