Densely　connected　convolutional　networks　(DenseNet)　have　reached　unprecedented　parameter-performance　efficiencies　and　alleviated　problems　of　vanishing　gradients　by　concatenating　each　layer　to　every　other　layer.　However,　with　the　increase　in　network　depth,　the　cross-channel　interaction　of　dense　blocks　has　become　increasingly　complex.　Hence,　it　is　now　more　difficult　to　optimize　networks.　Moreover,　the　way　combining　features　in　DenseNet　restricts　its　flexibility　and　scalability　in　learning　more　expressive　combination　strategies.　In　this　study,　we　aim　to　answer　the　question　of　how　to　simultaneously　ensure　the　benefits　of　feature　reuse,　reduce　the　complexity　of　cross-channel　interactions,　and　increase　the　flexibility　of　the　network.　Hence,　the　components　of　DenseNet　are　refined　and　then　used　as　a　basis　to　develop　a　universal　densely　connected　convolutional　network　(UDenseNet).　Based　on　the　proposed　architecture,　the　impact　of　different　component　configurations　on　the　network　performance　is　empirically　analyzed　to　determine　the　optimal　architectural　configuration.　Extensive　experiments　are　conducted　to　validate　the　proposed　UDenseNet　on　benchmark　datasets　(CIFAR,　SVHN　and　ImageNet).　Results　show　that,　compared　to　most　other　methods,　the　proposed　UDenseNet　can　significantly　improve　performance　in　image　recognition　tasks.　©　2020,　Springer　Nature　Switzerland　AG.