The airborne transient electromagnetic method is an important electromagnetic exploration technology, which obtains the information about the electrical structure of the earth through inversion.But due to the slow progress of the forward, the inversion will consume a significant amount of time.Aiming at the problem of long computation time of traditional forward method, this paper proposes a deep learning-based fast forward method for airborne transient electromagnetic.The method first uses the traditional finite volume method to calculate the induced electromotive force of a large number of different ground resistivity models to form a training dataset; then it designs a ResNet-UNet deep neural network; then it trains the network with the training dataset; finally, it inputs the ground resistivity model into the trained neural network to obtain the forward results.In order to verify the accuracy and efficiency of the method, the forward results of the ResNet-UNet deep neural network and the traditional finite volume method are compared.The experimental results show that the average relative errors of the entire validation set is less than 1.2%, with 87% of the average relative errors falling within the range of 0.1% to 0.3%, and the speed of deep neural network forward is about 2934 times higher than that of the traditional finite volume method, which significantly improves the forward efficiency of the airborne transient electromagnetic.The method is capable of fast forward of airborne transient electromagnetic, which can be put into the existing inversion framework to accelerate the inversion speed of large datasets.