Clustering　is　a　fundamental　and　hot　issue　in　the　unsupervised　learning　area.　With　the　rapid　development　of　deep　learning　and　graph　neural　networks　(GNNs)　techniques,　researchers　have　proposed　a　series　of　effective　clustering　methods.　However,　most　existing　approaches　adopt　a　conventional　graph　to　aggregate　the　neighborhood　information,　where　only　the　pairwise　relations　are　considered.　Moreover,　the　redundancy/noise　in　the　raw　data　samples　may　result　in　less　accurate　sample　relations　and　inferior　clustering　results.　In　this　paper,　we　proposed　a　new　GNNs　based　clustering　method,　which　adopts　the　hypergraph　learning　approach　to　explore　the　high-order　relationship　for　accurate　relation　learning.　Specifically,　we　first　construct　two　hypergraph　representations　based　on　the　topology　feature　and　attribute　feature　from　data　samples.　Then,　a　self-supervised　structure　is　integrated　to　learn　a　cross-correlation　matrix　from　the　original　hypergraph　to　act　as　a　higher-order　neighborhood　with　reduced　redundancy　and　noise.　Finally　the　embedding　representation　of　the　clustering　space　is　learned　in　the　graph　convolution.　The　proposed　method　has　been　evaluated　on　six　public　datasets　for　clustering　tasks.　Experimental　results　show　that　our　proposed　method　outperforms　the　state-of-the-art　ones.　©　2022　IEEE.