This article proposes to identify and recommend scientific workflows to promote their reuse and repurposing. Specifically, a scientific workflow is converted into a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows in order to construct a scientific workflow network model. A graph-skeleton based clustering method is adopted for grouping layer hierarchies into clusters. Barycenters in clusters are identified for facilitating cluster identification and workflow ranking and recommendation. Experimental result shows that this technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows