In view of the inherent coherent speckle noise in Synthetic-aperture radar (SAR) images, which affects the accuracy and accuracy of change detection, this paper proposes a change detection method for SAR images based on difference map construction and fusion. This method preprocesses SAR images through L-SRAD hybrid filtering, uses wavelet fusion algorithm based on edge pre-detection to achieve the fusion of logarithmic hyperbolic cosine ratio difference map DCLR and neighborhood ratio difference map DNR, and combines FCM algorithm and CWNN Convolutional neural network to detect changes in the fusion difference map. The FCM algorithm pre-classifies the fused difference map into three clusters, selects appropriate pre-classification results as training samples to train the CWNN model, and finally uses the CWNN model to perform secondary classification on the pre-classification results to obtain the final change detection map. Comparative experiments were conducted on the Bern dataset, and the experimental results showed that this method has strong change detection ability, with a change detection accuracy of 99.67%.