Visual　Question　Answering　(VQA)　tasks　must　provide　correct　answers　to　the　questions　posed　by　given　images.　Such　requirement　has　been　a　wide　concern　since　this　task　was　presented.　VQA　consists　of　four　steps:　image　feature　extraction,　question　text　feature　extraction,　multi-modal　feature　fusion　and　answer　reasoning.　During　multimodal　feature　fusion,　outer　product　calculation　is　used　in　existing　models,　which　leads　to　excessive　model　parameters,　high　training　overhead,　and　slow　convergence.　To　avoid　these　problems,　we　applied　the　Variational　Autoencoder　(VAE)　method　to　calculate　the　probability　distribution　of　the　hidden　variables　of　image　and　question　text.　Furthermore,　we　designed　a　question　feature　hierarchy　method　based　on　the　traditional　attention　mechanism　model　and　VAE.　The　objective　is　to　investigate　deep　questions　and　image　correlation　features　to　improve　the　accuracy　of　VQA　tasks.　©　Springer　Nature　Switzerland　AG　2019.