Different　process　parameters　have　a　huge　impact　on　the　physical　and　chemical　properties　of　the　LiPON　thin　films　synthesized　by　magnetron　sputtering.　It　has　great　significance　to　model　the　synthesis　process　for　strengthening　the　understanding　of　internal　principles　and　improving　the　properties　of　the　thin　films.　Transfer　learning　can　improve　model　ac　curacy　and　generalization　ability　by　mining　information　in　historical　data　sets,　so　as　to　better　find　good　process　parameters.　This　paper　takes　the　datasets　of　LiPON　synthesized　by　magnetron　sputtering　in　literatures　as　examples　to　explore　the　influ　ence　of　target-substrate　distance,　sputtering　power,　and　sputtering　pressure　on　the　ion-conductivity　of　LiPON　films.　Compar　ing　with　ordinary　machine　learning,　the　transfer　learning　model　improves　by　more　than　30%　in　multiple　error　metrics.　The　built　model　recommended　the　optimal　parameters　combination　after　traversing　parameters　space,　and　the　predicted　ion-con　ductivity　of　LiPON　film　is　2.04　μS/cm,　which　is　better　than　the　maximum　value　in　the　literature.　The　mapped　contour　graph　of　process　parameters　and　performance　recommended　for　a　process　parameter　range,　and　the　performance　of　film　is　good　and　stable　within　the　range.　The　analysis　of　variance　and　actual　samples　prove　that　the　method　is　practical.　©　2023　Chinese　Institute　of　Electronics.　All　rights　reserved.