With　the　powerful　learning　ability　of　deep　convolutional　networks,　deep　clustering　methods　can　extract　the　most　discriminative　information　from　individual　data　and　produce　more　satisfactory　clustering　results.　However,　existing　deep　clustering　methods　usually　ignore　the　relationship　between　the　data.　Fortunately,　the　graph　convolutional　network　can　handle　such　relationships,　opening　a　new　research　direction　for　deep　clustering.　In　this　paper,　we　propose　a　cross-attention　based　deep　clustering　framework,　named　Cross-Attention　Fusion　based　Enhanced　Graph　Convolutional　Network　(CaEGCN),　which　contains　four　main　modules:　the　cross-attention　fusion　module　which　innovatively　concatenates　the　Content　Auto-encoder　module　(CAE)　relating　to　the　individual　data　and　Graph　Convolutional　Auto-encoder　module　(GAE)　relating　to　the　relationship　between　the　data　in　a　layer-by-layer　manner,　and　the　self-supervised　model　that　highlights　the　discriminative　information　for　clustering　tasks.　While　the　cross-attention　fusion　module　fuses　two　kinds　of　heterogeneous　representation,　the　CAE　module　supplements　the　content　information　for　the　GAE　module,　which　avoids　the　over-smoothing　problem　of　GCN.　In　the　GAE　module,　two　novel　loss　functions　are　proposed　that　reconstruct　the　content　and　relationship　between　the　data,　respectively.　Finally,　the　self-supervised　module　constrains　the　distributions　of　the　middle　layer　representations　of　CAE　and　GAE　to　be　consistent.　Experimental　results　on　different　types　of　datasets　prove　the　superiority　and　robustness　of　the　proposed　CaEGCN.