The work aims to propose a dense convolutional spiking neural network (DCSNN) model for steel defect identification based on spiking neural network aiming at the problems of insufficient utilization of feature images, low recognition accuracy and numerous parameters of existing steel defect identification algorithms, so as to reduce system consumption and memory occupation. Firstly, convolutional coding was used to extract and encode the features of the input images. Secondly, the dense convolutional spiking neural network was constructed by the dense connection algorithm to realize the reuse of features and suppress the disappearance of gradients. Then, the network was trained by alternative gradient descent algorithm. Finally, the test was carried out on the strip steel dataset to realize the defect identification of strip steel. The experimental results indicated that the accuracy of DCSNN on the test set was 98.61% and the number of parameters was 5 000. The proposed model shows a good effect on the identification of steel surface defects.