The purpose of ranking aggregation (or fusion) is to combine multiple rankings to a consensus one. In the ranking aggregation, some of the items’ preference orders are easy to distinguish, however, some others’ are not. To specifically compare the ambiguous items, i.e., the items whose aggregated preference orders are difficult to distinguish, is helpful for ranking aggregation. In this paper, a new hierarchical ranking aggregation method is proposed. The items whose preference orders are easy to distinguish are first divided into different ranking levels (i.e., the ordered items subsets), and the ambiguous items are put into the same ranking level. The items in high ranking levels are ranked higher than the items in low ranking levels in the aggregated ranking. Then the items in the same ranking level are further compared and divided into multiple ranking sub-levels. The aggregated ranking is generated hierarchically by dividing the same ranking levels’ (or sub-levels’) items into sub-levels until each sub-level only includes one item. Furthermore, we discuss the way of using the insertion sort method for merging the adjacent levels’ rankings to improve the quality of the aggregated ranking. The experiments and simulations show that our new hierarchical methods perform well in ranking aggregation.