In　recent　years,　convolutional　neural　network　(CNN),　as　a　representative　neural　networks　in　the　area　of　deep　learning　technology,　has　been　widely　used　in　machine　vision　such　as　object　recognition　and　image　processing.　In　this　paper,　we　introduce　a　more　flexible　and　more　accurate　recurrent　convolutional　neural　network　(RCNN)　than　traditional　CNN　for　object　recognition.　But　RCNN　is　restricted　to　locality　and　lacks　the　understanding　of　the　global　context.　Inspired　by　the　research　of　cognitive　science　and　neuroscience,　we　put　forward　a　recurrent　convolutional　neural　network　integrated　with　self-attention　mechanism　(A-RCNN)　to　address　this　issue.　Unlike　most　methods　that　combine　self-attention　as　an　enhancement　of　convolutional　layers　with　CNN,　our　method　introduces　the　self-attention　mechanism　as　an　independent　layer　into　the　RCNN.　We　evaluate　our　constructed　A-RCNN　on　the　publicly　available　CIFAR-10,　CIFAR-100　and　MNIST　datasets.　The　analysis　of　experimental　results　shows　that　our　proposed　A-RCNN　has　significantly　improved　accuracy　on　object　recognition　tasks　compared　with　the　original　RCNN　and　CNN.　©　2022　IEEE.