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This paper focuses on domain adaptation in a semantic segmentation task. Traditional methods regard the source domain and the target domain as a whole, and the image matching is determined by random seeds, leading to a low degree of consistency matching between domains and interfering with the reduction in the domain gap. Therefore, we designed a two-step, three-level cascaded domain consistency matching strategy—co-occurrence-based consistency matching (COCM)—in which the two steps are: Step 1, in which we design a matching strategy from the perspective of category existence and filter the sub-image set with the highest degree of matching from the image of the whole source domain, and Step 2, in which, from the perspective of spatial existence, we propose a method of measuring the PIOU score to quantitatively evaluate the spatial matching of co-occurring categories in the sub-image set and select the best-matching source image. The three levels mean that in order to improve the importance of low-frequency categories in the matching process, we divide the categories into three levels according to the frequency of co-occurrences between domains; these three levels are the head, middle, and tail levels, and priority is given to matching tail categories. The proposed COCM maximizes the category-level consistency between the domains and has been proven to be effective in reducing the domain gap while being lightweight. The experimental results on general datasets can be compared with those of state-of-the-art (SOTA) methods.