In the linear injection models of traditional Component Substitution （CS） and Multi-Resolution Analysis （MRA） in remote sensing image pansharpening， the relative spectral response of the sensor used for pansharpening is not considered. At the same time， the insufficient feature extraction of the original images caused by the deep learning-based methods results in the loss of spectral and spatial information in the fusion results. Aiming at the above problems， a pansharpening method combining traditional and deep learning methods was proposed， namely CMRNet. Firstly， CS and MRA were combined with Convolutional Neural Network （CNN） to achieve nonlinearity and improve the performance of pansharpening method. Secondly， the Residual Channel （RC） blocks were designed to realize the fusion and extraction of multi-scale feature information， and the feature maps of different channels were assigned different weights by using Channel Attention （CA） adaptively to learn more effective information. Experiments were conducted on QuickBird and GF1 satellite datasets. Experimental results show that on downscale QuickBird and GF1 datasets， compared with the classic method PanNet， CMRNet has the Peak Signal-to-Noise Ratio （PSNR） increased by 5.48% and 9.62% respectively， and other indexes also improved significantly， which verifies that CMRNet can achieve a better pansharpening effect.