Type of tire defects directly determines whether the tire is defective products or waste, which has important reference value for tire grading, it is vital to explore high performance tire defect classification method. First, collecting five common types of defects and a normal images from a typical tire manufacturing, namely beltforeignmatter, sidewallforeignmatter, beltjointopen, cordsdistance, bulksidewall and normalcords, was used to perform the tire defect classification experiments. And downsampling or upsampling the images in the dataset to a fixed resolution of 127×127. And then designing depth network which contains 5 convolutional layers, 3 maxpooling layers, 3 fullyconnected layers. Finally, training and testing the designed depth network with defect samples collected. Experimental results showed that the method proposed has higher recognition rates for tire defects than other algorithms, the averaged rate of recognition is high to 96.51%.