Prediction　of　survival　risk　in　cancer　patients　is　crucial　for　understanding　the　underlying　mechanisms　of　canceration　in　different　stages.　Previous　studies　mainly　relied　on　single-modal　omics　data　due　to　technological　constraints.　However,　with　the　increasing　availability　of　cancer　omics　data,　researchers　have　focused　on　the　use　of　multi-omics　and　multimodal　data　for　survival　analysis.　The　application　of　deep　learning　methods　has　become　an　option　for　the　prediction　of　clinical　risk.　Recent　advances　in　the　attention　mechanism　and　the　variational　autoencoder　(VAE)　have　made　them　promising　for　analyzing　cancer　omics　data.　However,　VAE　has　limitations　in　disregarding　the　importance　of　different　features　between　modalities,　and　the　introduction　of　an　attention　mechanism　could　address　this　limitation.　In　this　study,　we　propose　a　Multimodal　Attention-based　VAE　(MAVAE)　deep　learning　framework　using　cross-modal　multihead　attention　to　integrate　cancer　multi-omics　data　for　clinical　risk　prediction.　We　evaluated　our　approach　on　eight　TCGA　datasets.　We　find　that　(1)　MAVAE　outperforms　traditional　machine　learning　and　recent　deep　learning　methods;　(2)　Multi-modal　data　yields　better　classification　performance　than　single-modal　data;　(3)　The　multi-head　attention　mechanism　improves　the　decision-making　process;　(4)　Clinical　and　genetic　data　are　the　most　important　modal　data.　Our　implementation　of　MAVAE　is　available　at　https://github.com/wenwenmin/MAVAE.　©　2023　IEEE.