In order to overcome the shortcomings as spectral distortion and poor target content saliency of the fused image by ignoring spectral features in current visible and infrared image fusion methods, the infrared and visible image fusion algorithm based on nonsubsampled contourlet transform coupled with feature selection mechanism is proposed in this paper. Firstly, the visible and infrared images are calculated by NSCT to separate it into different image coefficients. Then, the information entropy function is used to measure the richness of the image information content for forming the fusion coefficient of low-frequency coefficient, which can obtain the fusion low-frequency coefficient with rich information such as infrared target. The neighborhood information of pixels was used to measure the definition feature of image, and the mean function was introduced to measure the spectral feature of image. Through the definition feature and the spectral feature of image, the feature selection mechanism was constructed to select the ideal high-frequency coefficient fusion function from the image, and obtain the fused high-frequency coefficient that takes into account both the detailed characteristics and the spectral characteristics. Finally, the experimental results show that compared with the existing fusion algorithm, the proposed algorithm has better spectral characteristics, significant target content and better fusion performance.