The current extraction of image information is constrained by the transmission of massive data and the limitations of channel communication capabilities. To address this, a multi-layer image information extraction system has been constructed to overcome the limitations of transmission time and communication capacity. Based on information entropy theory, a multi-layer target image information extraction algorithm is established, using the minimum information entropy of image-based sensor information as input. By combining image feature engineering, the algorithm enhances the feature extraction and inference of images. It also utilizes sensor image data to extract the minimum volume of key information from images. Experiments have verified that this algorithm can increase the image information compression ratio to 10⁶ without losing complete and effective information. This effectively solves the problem of real-time and reliable long-distance image information transmission with low data volume and low bandwidth.