The　heat　transfer　of　supercritical　R134a　in　a　horizontal　internally　ribbed　tube　was　predicted　by　using　a　back　propagation　artificial　neural　network　(ANN).　The　network　was　trained　based　on　4440　experimental　data　points.　The　effects　of　the　network　input　parameters,　data　division　method,　training　function,　transfer　function,　number　of　hidden　layers,　and　number　of　neurons　on　the　prediction　results　were　analyzed　in　detail,　and　a　new　empirical　formula　for　determining　the　optimal　number　of　neurons　was　proposed.　The　prediction　results　by　the　network　were　then　compared　with　those　of　four　traditional　classical　correlations.　The　results　revealed　that　the　mean　absolute　errors　of　the　ANN　for　predicting　Nutop　and　Nubottom　were　only　35.28%　and　33.03%,　respectively,　of　those　of　the　traditional　model.　Furthermore,　99.02%　of　Nu　could　be　predicted　with　deviations　smaller　than　30%　by　the　ANN,　whereas　only　88.7%　could　be　predicted　by　traditional　correlations,　indicating　that　the　ANN　has　a　higher　prediction　accuracy.　The　present　study　provides　a　useful　reference　for　the　application　and　optimization　of　ANNs　for　heat　transfer　prediction　and　the　design　of　supercritical　fluid　heaters.