Hyperspectral imagery holds a significant level of importance as it provides detailed information about various objects owing to the acquisition of narrow-band information. A hyperspectral image encompasses multiple spectral bands and involves intricate processes for the identification and classification of objects manually. The existing hyperspectral image classification methods experience limited spatial resolution and reduced accuracy in the classification process. To overcome this issue, this research article presents a hybrid deep convolutional neural network (HDCNN) for the automatic processing and analysis of hyperspectral images. The HDCNN consists of fuzzy-based convolutional neural networks (FBCNNs) and variational autoencoders (VAEs). Furthermore, non-negative matrix factorization is utilized for the extraction of features and the reduction of dimensionality. In this approach, the FBCNN is employed for the automatic classification of hyperspectral images, taking into account the uncertainty and vagueness present in the data. The VAE is utilized for the detection of anomalies and the generation of new data with meaningful characteristics. Based on the experimental findings, it has been observed that the FBCNN yields enhanced accuracy in classification and exhibits superior performance in terms of accuracy, precision, sensitivity, and recall. The proposed FBCNN exhibits 97.1% of accuracy, 91.47% of sensitivity, 90.86% of precision, and 88.5% of recall.