Background:　Medication　recommendation　based　on　electronic　medical　record　(EMR)　is　a　research　hot　spot　in　smart　healthcare.　For　developing　computational　medication　recommendation　methods　based　on　EMR,　an　important　challenge　is　the　lack　of　a　large　number　of　longitudinal　EMR　data　with　time　correlation.　Faced　with　this　challenge,　this　paper　proposes　a　new　EMR-based　medication　recommendation　model　called　MR-KPA,　which　combines　knowledge-enhanced　pre-training　with　the　deep　adversarial　network　to　improve　medication　recommendation　from　both　feature　representation　and　the　fine-tuning　process.　Firstly,　a　knowledge-enhanced　pre-training　visit　model　is　proposed　to　realize　domain　knowledge-based　external　feature　fusion　and　pre-training-based　internal　feature　mining　for　improving　the　feature　representation.　Secondly,　a　medication　recommendation　model　based　on　the　deep　adversarial　network　is　developed　to　optimize　the　fine-tuning　process　of　pre-training　visit　model　and　alleviate　over-fitting　of　model　caused　by　the　task　gap　between　pre-training　and　recommendation.　Result:　The　experimental　results　on　EMRs　from　medical　and　health　institutions　in　Hainan　Province,　China　show　that　the　proposed　MR-KPA　model　can　effectively　improve　the　accuracy　of　medication　recommendation　on　small-scale　longitudinal　EMR　data　compared　with　existing　representative　methods.　Conclusion:　The　advantages　of　the　proposed　MR-KPA　are　mainly　attributed　to　knowledge　enhancement　based　on　ontology　embedding,　the　pre-training　visit　model　and　adversarial　training.　Each　of　these　three　optimizations　is　very　effective　for　improving　the　capability　of　medication　recommendation　on　small-scale　longitudinal　EMR　data,　and　the　pre-training　visit　model　has　the　most　significant　improvement　effect.　These　three　optimizations　are　also　complementary,　and　their　integration　makes　the　proposed　MR-KPA　model　achieve　the　best　recommendation　effect.