The traditional sparse representation(SR) block-processing strategy reduces the continuity of the image,which results in a serious loss of feature information.Therefore,an infrared and visible image fusion algorithm based on convolution sparse representation(CSR) and energy features is proposed.In this algorithm,the low-frequency subband of the non-subsampled contoured transform(NSCT) domain is decomposed into the low-frequency basic component and the detailed feature component,local Laplace energy method(LLE) and convolutional sparse representation are used for fusion respectively to obtain the low-frequency subband fusion image.Meanwhile,a new activity measurement method is constructed according to the underlying visual features to fuse the high-frequency subband.Finally,the low-frequency and high-frequency parts are reconstructed by NSCT inverse transformation.The experimental results show that this algorithm effectively combines the edge texture information of source images,and is superior to most existing algorithms in subjective and objective evaluation.