In the direction of binocular stereo matching in computer vision, deep learning algorithms based on neural networks require scene datasets for training and have poor generalization ability. In order to address these two problems, an iterative optimization algorithm of compatible solutions of deep scenes is proposed based on the ability of neural networks to simulate functions, and the algorithm requires no training on a dataset, with binocular images supervised by each other. The algorithm uses a scene location guessing network to simulate the compatible location space of a deep scene about the current binocular image, and a mutually supervised loss function matched with this network is used to guide the network to iteratively learn on the input binocular image by gradient descent. In addition, the feasible solution in the compatible location space of the deep scene is searched, and the whole process does not require training on the dataset. Comparison experiments with CREStereo, PCW-Net, CFNet, and other algorithms on Middlebury standard dataset images show that this algorithm has an average mismatching rate of 2.52% in non-occluded regions and 7.26% in all regions, which is lower than that of the other algorithms in the comparison experiments.