In　recent　years,　image　style　transfer　and　deep　learning　technology　have　developed　rapidly.　It　has　been　widely　used　in　many　fields,　such　as　film　and　TV　filters　and　art　creation.　However,　the　quality　of　the　generated　stylized　images　still　needs　improvement,　often　with　content　structure　distortion,　content　detail　loss,　lack　of　semantic　perception,　and　other　problems.　In　this　paper,　we　propose　an　image　style　transfer　method　based　on　local　matching　and　global　alignment.　The　local　matching　module　is　used　to　enhance　the　semantic　similarity　perception　and　improve　the　local　style.　The　global　alignment　module　improves　the　content　structure　distortion　problem　by　aligning　the　statistical　information　of　features.　The　integration　of　local　matching　information　and　global　alignment　information　can　better　balance　the　style　and　content　structure.　The　comparison　experiments　of　stylized　effects　demonstrate　that　our　approach　can　generate　high-quality　stylized　images.　©　2023　IEEE.