To solve the loss of detail information and insufficient feature extraction in the fusion results of infrared and visible light images， a deep learning network model for infrared and visible light image fusion with multi-scale densely connected attention is proposed. First， multi-scale convolution is designed to extract information of different scales in infrared and visible light images to increase the feature extraction range in the receptive field and overcome the problem of insufficient feature extraction at a single scale. Then， feature extraction is enhanced through a densely connected network， and an attention mechanism is introduced at the end of the encoding sub-network to closely connect the global context information and enhance the ability to focus on important feature information in infrared and visible light images. Finally， the fully convolutional layers that compose the decoding network are used to reconstruct the fused image. This study selects six objective evaluation indicators of image fusion， and the fusion experiments conducted on public infrared and visible light image datasets show that the proposed algorithm exhibits improved results compared with eight other methods. The structural similarity （SSIM）， spatial frequency （SF） indicators increase by an average of 0.26 and 0.45 times， respectively. The fusion results of the proposed method retain clearer edge and target information with better contrast and clarity， and are superior to the compared methods in both subjective and objective evaluations.