AutoEncoder （AE） based novelty detection method utilizes reconstruction error to classify the test samples to be normal or novel data. However， the above method produces very close reconstruction errors on normal data and novel data. Therefore， some novel data are easy to be misclassified as normal data. To solve the above problem， a novelty detection method composed of two parallel AEs and one Transformer network was proposed， namely Novelty Detection based on Dual Autoencoders and Transformer Network （DATN-ND）. Firstly， the bottleneck features of input samples were used by Transformer network to generate the bottleneck features with pseudo-novel data， thereby increasing the novel data information in the training set. Secondly， the bottleneck features with novel data information were reconstructed by the dual AEs to normal data as much as possible， increasing the reconstruction error difference between novel and normal data. Compared with MemAE （Memory-augmented AE）， DATN-ND has the Area Under the Receiver Operating Characteristic curve （AUC） improved by 6.8 percentage points， 12.0 percentage points， and 2.5 percentage points respectively on MNIST， Fashion-MNIST， and CIFAR-10 datasets. Experimental results show that DATN-ND can effectively make the difference of reconstruction error between normal data and abnormal data bigger.