Recently, deep learning networks for image compressive sensing have received a great deal of attention. Deep learning networks can achieve compressed sampling of images and reconstruct the original image from the sampled data. However, the existing compressive sensing algorithms cannot effectively extract original image information in image scenes with uneven information distribution, resulting in low reconstruction accuracy. To address the above problem, this paper proposes an image compressive sensing algorithm based on multi-channel sampling and attention reconstruction. The algorithm includes multiple sampling channels that can apply different sampling rates to different regions of the image according to visual saliency, so that the sampling data can contain more original image information. The reconstruction adopts the residual channel attention structure, which can adaptively adjust the channel features to improve the representation ability of the network. The comparative experiments show that the image compressive sensing algorithm based on multi-channel sampling and attention reconstruction proposed in this paper can achieve better reconstruction quality and visual perception.