In the existing weight-adaptive cross-scale algorithms, the same weight for the cost function of each scale is adopted, the different influence of each scale layer on the whole matching cost is missing, and thus the number of mismatching points increases. As for this problem, a weight-adaptive cross-scale algorithm framework for stereo matching is proposed. The cost matching is performed on different scales in the framework of unified cost aggregation function and the information entropy of each pixel window is used as the influence factor of the matching cost at each scale on the whole matching cost. At the same time, a regularization factor is added to the cost function to ensure the cost consistency at different scales for the same pixel. The proposed algorithm framework can be applied to the multi-scale algorithm of cost matching and improve the accuracy and robustness of the existing algorithms. Based on the proposed algorithm framework, the different cost aggregate functions are tested on the Middlebury dataset. To ensure the fairness of tests, as for each algorithm, there is no a subsequent parallax refinement step. The experimental results show that the proposed algorithm effectively improves the accuracy and robustness of multi-scale stereo matching.