Visual　Question　Answering　(VQA)　is　an　extremely　stimulating　and　challenging　research　area　which　requires　joint　image　content　and　language　understanding　to　answer　questions　about　a　given　image.　The　existing　VQA　models　have　made　many　efforts　in　the　direction　of　improving　the　ability　to　understand　images　and　achieved　good　results.　However,　these　models　ignore　the　relationship　between　the　image　and　the　corresponding　question,　which　is　strongly　correlated　when　the　data　is　collected　(i.e.,　the　question　asked　by　the　staff　should　be　consistent　with　the　current　image).　To　capture　the　essential　features　between　the　image　and　the　question,　we　propose　a　new　VQA　model　based　on　contrastive　learning　by　maximizing　mutual　information.　The　core　idea　of　our　model　is　to　maximize　the　mutual　information　between　question　features　and　corresponding　image　features,　and　minimize　the　mutual　information　between　question　features　and　their　irrelevant　image　features,　so　as　to　improve　the　ability　of　image　understanding　and　question　understanding　of　the　model　simultaneously.　The　experimental　results　indicate　that　the　feature　representation　learned　by　our　model　is　more　representative,　and　the　performance　of　our　model　on　VQA　v1.0　and　VQA　v2.0　datasets　is　better　than　the　baseline　model.　©　2021　IEEE.