Convolution neural network has been widely used in image processing. Meanwhile, different algorithms have different impact on network recognition rate. Based on this,we introduced wavelet decomposition theory and got that independent characteristics can express the original image more clearly，which proved by BP propagation algorithm and space vector theory. Because wavelet decomposition reduced the correlation between the kernel and extracted more independent,comprehensive features with less convolution kernel,the network performance is improved.Recognition experiments are conducted on MNIST,CIFAR-10 and CK standard database,the results show that the algorithm proposed in this paper can achieve higher recognition rate under the condition of different kernel size and can obtain the recognition rate as the traditional algorithm with fewer iteration times and shorter training time.At last,this algorithms was applied in the fault identification of insulators and achieved good results.
