Graph　data　presents　a　vast　landscape　for　real-world　applications.　Current　graph-level　clustering　approaches　predominantly　utilize　graph　neural　networks　to　capture　the　intricate　structural　information　for　graph　data.　However,　a　significant　challenge　arises　in　effectively　integrate　structural　and　feature　information　under　the　prevalent　noise　in　the　real-world　scenario.　The　advent　of　masking　strategies　has　marked　significant　strides　in　boosting　model　robustness,　accommodating　incomplete　data,　and　enhancing　generalization　capabilities.　Yet,　research　attention　on　leveraging　mask　strategy　for　facilitating　graph-level　clustering　is　still　limited.　In　this　paper,　we　introduce　a　novel　graph-level　clustering　method,　towards　adaptive　masked　structural　learning　for　graph-level　clustering.　The　method　performs　adaptive　masking　through　reconstruction　loss,　and　jointly　adaptive　mask　representation　learning　and　clustering　in　an　end-to-end　unsupervised　framework.　The　mutual　information　between　maximized　the　entire　graph　and　substructure　representations　is　also　utilized　to　learn　to　generate　cluster-oriented　graph-level　representations.　Extensive　experiments　on　eight　real　graph-level　benchmark　datasets　demonstrate　the　effectiveness　and　superiority　of　the　proposed　method.