Ubiquitination　is　a　very　important　protein　post-translational　modification　in　humans,　which　is　closely　related　to　many　human　diseases　such　as　cancers.　Although　some　methods　have　been　elegantly　proposed　to　predict　human　ubiquitination　sites,　the　accuracy　of　these　methods　is　generally　not　very　satisfactory.　In　order　to　improve　the　prediction　accuracy　of　human　ubiquitination　sites,　we　propose　a　new　ensemble　method　HUbipPred,　which　takes　the　binary　encoding　and　physicochemical　properties　of　amino　acids　as　training　features,　and　integrates　two　intensively　trained　convolutional　neural　networks　and　two　recurrent　neural　networks　to　build　the　model.　Finally,　HUbiPred　achieves　AUC　values　of　0.852　and　0.844　in　five-fold　cross-validation　and　independent　tests,　respectively,　which　greatly　improves　the　prediction　accuracy　compared　to　previous　predictors.　We　also　analyze　the　physicochemical　properties　of　amino　acids　around　ubiquitination　sites,　study　the　important　roles　of　architectures　(i.e.,　convolution,　long　short-term　memory　(LSTM)　and　fully　connected　hidden　layers)　in　the　networks　for　prediction　performance,　and　also　predict　potential　ubiquitination　sites　in　humans　using　HUbiPred.　The　training　and　test　datasets,　predicted　human　ubiquitination　sites,　and　source　codes　of　HUbiPred　are　publicly　available　at　https://github.com/amituofo-xf/HUbiPred.