Convolutional　neural　network　(CNN)　is　widely　applied　to　different　areas　due　to　good　recognition　performance.　However,　convolution　operation　is　a　complex　computation　and　consumes　the　bulk　of　processing　time　for　CNN.　It　is　still　a　hot　problem　how　to　develop　a　novel　model　with　good　recognition　performance　for　deep　learning.　Here,　we　propose　a　novel　model,　namely,　two-dimensional　perceptron　(TDP),　to　get　direct　input　of　two-dimensional　data　for　further　processing.　A　TDP　has　a　new　network　architecture　and　an　innovative　computation　process　of　hidden　neurons.　In　cases　with　the　same　number　of　hidden　neurons,　compared　with　multilayer　perceptron　(MLP),　TDP　achieves　good　recognition　performance　with　1　x　-36　x　speedup　and　a　decrease　of　parameters　by　exceeding　97%　on　MNIST　and　COIL-20　datasets.　Meanwhile,　TDP　obtains　1%-32%　improvement　of　recognition　accuracy　in　comparison　to　CNN　on　CIFAR-10　and　SVHN　datasets.　Furthermore,　on　INFUSE　dataset,　TDP　has　an　increase　of　F1　score　by　up　to　almost　11%　in　comparison　with　MLP　and　CNN.　The　results　indicate　that　TDP　is　a　promising　and　novel　model　with　excellent　recognition　performance.　(C)　2020　Elsevier　B.V.　All　rights　reserved.