Image　stitching　can　be　employed　to　stitch　images　taken　from　different　times,　perspectives,　or　devices　into　a　panorama　with　a　wider　view.　However,　the　imaging　specification　of　images　to　be　stitched　is　strict.　If　the　imaging　specification　is　not　satisfied,　artefacts　caused　by　inaccurate　alignment　and　unnatural　distortion　will　occur.　Semantic　segmentation　can　solve　the　classification　problem　at　the　pixel　level;　however,　image　stitching　significantly　depends　on　the　accuracy　of　feature　points.　Therefore,　this　paper　proposes　an　image　stitching　algorithm　based　on　semantic　segmentation　to　guide　feature　point　classification　and　seam　fusion.　First,　the　images　are　recognized　by　a　cascade　semantic　segmentation　network,　and　the　image　feature　points　are　classified.　Thereafter,　the　corresponding　homography　transformations　are　calculated　using　different　class　feature　points,　and　the　best　homography　mapping　for　the　entire　target　image　is　selected.　Finally,　a　seam-cutting　algorithm　based　on　semantic　segmentation　is　used　to　compute　the　seam,　and　a　feathering　Poisson　fusion　with　distance　transformation　is　used　to　eliminate　artefacts　and　light　differences.　Experiments　show　that　the　algorithm　can　generate　transitional　natural　and　perceptual　stitching　results　even　under　the　influence　of　perspective　and　light　differences.　©　The　Author(s)　2021.