The introduction of auxiliary task information is helpful for the stereo matching model to understand the related knowledge, but the complexity of model training increases. In order to solve the problem of dependence on extra label data during model training, we proposed an algorithm based on multi-task learning for stereo matching by using the autocorrelation of binocular images. This algorithm introduces the edge and feature consistency information in the multi-level progressive refinement process and updates the disparity map in a cyclic and iterative manner. According to the local smoothness of disparity and the consistency of left and right features of binocular images, a loss function is constructed to guide the model to learn the edge and feature consistency information without relying on additional label data. A spatial pyramid pooling with scale attention is proposed to enable the model to determine the importance of different scale features based on the local image features in different areas. The experimental results show that the introduction of auxiliary tasks not only improves the accuracy of disparity maps, but also provides a significant basis for the trusted regions of disparity maps. It can also be used to determine the single-view visible areas in unsupervised learning. The proposed algorithm has certain competitiveness in terms of accuracy and operating efficiency on the KITTI2015 test dataset.