Convolutional　neural　networks　(CNNs)　have　proven　to　be　very　successful　in　learning　task　specific　computer　vision　features.　To　integrate　features　from　different　layers　in　standard　CNNs,　we　present　a　fusing　framework　of　shortcut　convolutional　neural　networks　(S-CNNs).　This　framework　can　fuse　arbitrary　scale　features　by　adding　weighted　shortcut　connections　to　the　standard　CNNs.　Besides　the　framework,　we　propose　a　shortcut　indicator　(SI)　of　binary　string　to　stand　for　a　specific　S-CNN　shortcut　style.　Additionally,　we　design　a　learning　algorithm　for　the　proposed　S-CNNs.　Comprehensive　experiments　are　conducted　to　compare　its　performances　with　standard　CNNs　on　multiple　benchmark　datasets　for　different　visual　tasks.　Empirical　results　show　that　if　we　choose　an　appropriate　fusing　style　of　shortcut　connections　with　learnable　weights,　S-CNNs　can　perform　better　than　standard　CNNs　regarding　accuracy　and　stability　in　different　activation　functions　and　pooling　schemes　initializations,　and　occlusions.　Moreover,　S-CNNs　are　competitive　with　ResNets　and　can　outperform　GoogLeNet,　DenseNets,　Multi-scale　CNN,　and　DeepID.　(c)　2021　Elsevier　Inc.　All　rights　reserved.