The　objective　of　image　captioning　involves　empowering　computers　to　autonomously　produce　human-like　sentences　that　depict　a　provided　image.　To　address　the　issues　of　insufficient　accuracy　in　image　feature　extraction　and　underutilization　of　visual　information,　we　propose　a　Swin　Transformer-based　image　captioning　model　with　feature　enhancement　and　multi-stage　fusion.　First,　the　Swin　Transformer　is　employed　in　the　capacity　of　an　encoder　for　the　purpose　of　extracting　image　features,　and　feature　enhancement　is　adopted　to　capture　more　information　about　image　features.　Then,　a　multi-stage　image　and　semantic　fusion　module　is　constructed　to　utilize　the　semantic　information　from　past　time　steps.　Finally,　LSTM　is　used　to　decode　the　semantic　and　image　information　and　generate　captions.　The　proposed　model　achieves　better　results　in　baseline　tests　on　the　public　datasets　Flickr8K　and　Flickr30K.　©　2023　IEEE.