Evaluation　of　the　structural　similarity　or　structural　domains　is　a　fundamental　step　for　characterizing　fractured　rock　masses.　In　this　research,　the　division　of　rock　masses　into　structural　domains　is　treated　as　a　multi-parameter　clustering　problem.　The　fuzzy　spectral　clustering　method　is　used　and　proved　to　be　an　effective　approach　for　measuring　the　structural　similarity　of　jointed　rock　masses,　in　which　six　fracture　parameters　(i.e.,　orientation,　aperture,　roughness,　fracture　intensity　P-20,　filling,　and　groundwater　condition)　are　selected.　The　results　suggest　that　the　study　area,　located　at　the　Songta　dam　site　of　China,　could　be　grouped　into　three　structural　domains　based　on　over　1300　fractures　collected　from　6　adjacent　exploration　tunnels.　The　results　of　the　proposed　method　are　validated　by　the　Kolmogorov-Smirnov　statistical　test　through　quantifying　the　degree　of　similarity　between　fracture　parameters　collected　from　different　areas.　The　results　show　that　the　fuzzy　spectral　clustering　could　provide　reliable　results　which　agree　well　with　those　obtained　by　the　Kolmogorov-Smirnov　test.　Moreover,　compared　with　the　K-means　algorithm,　the　new　method　could　perform　better　in　identifying　structural　domains.