In the field of computational ghost imaging based on compressed sensing, the design of the measurement matrix has always been a subject of research. The ideal measurement matrix must possess high sampling efficiency, good reconstruction effect, and low hardware-implementation difficulty. To reduce the difficulty of designing and implementing the measurement matrix, this paper proposes a method for constructing a binary measurement matrix based on deep learning. This method uses convolution to simulate the compressed sampling process of the image and trains the image data through the designed sampling network to adaptively and iteratively update the measurement matrix. The results of the simulation and experiments show that the constructed measurement matrix can obtain high-quality reconstructed images under low sampling rate, which further facilitate the practical application of computational ghost imaging.