A multi-level fusion method for laser images based on visual communication technology was designed to achieve outstanding visual communication effects. Firstly, the improved single scale Retinex algorithm is used to extract the reflection image of the original laser image, and the multi-scale color image obtained through reconstruction using the Gaussian Laplace algorithm is used to enhance the original laser image. Then, a deep stacked convolutional neural network is used to obtain high and low frequency images, and high-frequency images are fused based on the maximum local variance. Low frequency images are fused based on the comparison between matching degree and threshold, The final experimental results show that when the number of stacked CNN is 4, the visual communication effect of the fused laser image is the best. The enhanced laser image has rich local detail information and good color fullness, and the maximum grayscale frequency of the fused image is only 0.015.