Application of deep learning in the area of automatic modulation classification (AMC) is still evolving. An unsupervised sparse‐autoencoder‐based deep neural network (SAE‐DNN) is proposed to deal with the problem of AMC for much neglected frequency selective fading scenarios with Doppler shift. The authors propose a set of low complexity spectral and cumulant based features for training SAE‐DNN. The network is designed using forced dimensionality reduction and sparsity constraint to achieve a low complexity solution with improved ability to learn more refined and robust features from the input training data. A unique training method is presented in this study which incorporates a range of SNR values for the entire span of the training dataset, as compared to the conventional approach which only uses a single SNR value for all the training examples. A comprehensive performance analysis shows that the proposed method outperforms many conventional counterparts in the literature. Generalisation test verifies that network is feasible for all channel conditions. A robust classification behaviour is observed against phase‐frequency impairments and Doppler shift for frequency selective fading scenarios.