With　the　growth　of　high　technology,　the　digitization　of　art　and　cultural　relics　is　no　longer　a　problem.　At　present,　ceramic　enterprises　are　gradually　evolving　from　large-scale　customization　to　personalized　customization,　and　the　demand　for　upgrading　the　intelligent　manufacturing　industry　is　increasing.　A　personalized　ceramic　pattern　generation　algorithm　is　on　the　verge　of　emergence.　With　the　theoretical　growth　of　neural　networks　and　the　improvement　of　hardware　computing　power,　machine　learning　(ML)　and　deep　learning　(DL)　have　achieved　remarkable　achievements　in　multiple　fields.　Image　feature　extraction　technology　and　automatic　generation　technology　have　both　received　great　attention　from　the　academic　community.　Generative　Adversarial　Networks　(GANs)　have　promoted　research　applications　in　image　generation　and　restoration,　text　generation,　video　prediction,　and　style　transfer,　demonstrating　their　enormous　potential　in　image　processing.　This　article　designs　an　ML　based　algorithm　for　automated　production　of　ceramic　patterns.　By　using　ML　technology,　useful　features　are　automatically　extracted　from　a　large　amount　of　ceramic　pattern　data,　and　these　features　are　used　to　train　the　model,　thereby　generating　new　ceramic　patterns.　The　experimental　results　show　that　the　algorithm　can　be　well　applied　to　practical　cases　and　effectively　achieve　automated　production　of　ceramic　patterns.　©　2024　IEEE.