Image　style　transfer　is　a　method　that　extracts　its　style　from　the　style　image　and　applies　this　style　to　content　image.　Since　the　introduction　of　neural　style　transfer,　the　field　of　style　transfer　has　developed　rapidly,　and　many　new　methods　have　been　proposed.　There　are　also　some　methods　based　on　feed-forward　networks,　but　these　methods　can　usually　only　transfer　one　style　or　several　styles.　And　these　methods　usually　use　feature　gram　matrix　to　calculate　style　loss.　However,　due　to　the　global　nature　of　the　gram　matrix,　some　local　details　cannot　be　stylized　well,　which　is　prone　to　distortion　and　artifacts.　In　this　work,　we　propose　an　arbitrary　style　transfer　method　based　on　feed-forward　network,　in　which　the　gram　matrix　and　feature　similarity　are　used　together　to　calculate　the　style　loss.　The　loss　calculated　by　the　similarity　between　features　(called　contextual　loss　in　this　paper)　is　minimized　to　produce　stylized　images　with　better　details　and　fewer　artifacts.　Experimental　results　and　user　study　prove　that　our　method　has　achieved　the　state-of-the-art　performance　compared　with　existing　arbitrary　style　transfer　methods.　©　2021,　Springer　Nature　Singapore　Pte　Ltd.