Aiming at problem of high difficulty of X-ray welds defects detection and classification

a deep convolutional neural networks（ CNN） structure is designed

which is based on improvement of typical CUDACONVNET CNN. The hierarchical structure and parameter setting of CNN are researched under the premise in weld features of original images can be maximum extracted

on the basis of image preprocessed.Meanwhile

further estimate the deep learning mode by comparing with the recognition algorithm of support vector machine（ SVM）.The research results show that for large samples

the structure and its algorithm of the improved deep CNN have advantages in images feature expression and recognition ability while its computation samples are inversely proportional to the error rate

which means the larger the samples number is

the lower the recognition error rate is.In addition

the network structure has a high correct rate for classification recognition of images.