Currently, there are some difficulties in the fusion of infrared and visible light images, resulting in low accuracy, large errors, and low fusion efficiency. In order to solve the problems in the current process of infrared and visible light images, a fusion method for infrared and visible light images based on feature extraction using convolution- al neural networks was designed. Firstly, the infrared and visible light images of the object were collected separately, and the original image was preprocessed for denoising to improve the quality of the image. Then, convolutional neural networks were used to extract the fusion features of the infrared and visible light images, and the fusion results of the infrared and visible light images were obtained based on the features. Finally, simulation experiments were conducted, and the results showed that the fusion ratio of the fusion results of the infrared and visible light images using the pro- posed method increased by 0. 24, The average gradient value increased by 0. 22, resulting in higher image fusion qual- ity.