Seismic facies classification takes a two-step approach: attribute extraction and seismic facies analysis by using clustering algorithms, sequentially. In general, it is clear that the choice of feature extraction is critical for successful seismic facies analysis. However, the choice of features is customarily determined by the seismic interpreters, and so the clustering result is affected by the difference in the seismic interpreters’ experience levels. It becomes challenging to extract features and identify seismic facies simultaneously. We have introduced deep convolutional embedded clustering (DCEC), which aims to simultaneously learn feature representations and cluster assignments by using deep neural networks. Our method learns mapping from the data space to a lower dimensional feature space in which it iteratively optimizes a clustering objective by building a specific loss function. We apply the method to the Modified National Institute of Standards and Technology (MNIST) data, geophysical model data, and field seismic data. In the MNIST data, the DCEC method shows better latent space of clustering results than traditional clustering methods. In the geophysical model data, the accuracy of waveform classification based on DCEC method is higher than traditional clustering methods. The results from the seismic data demonstrate that selection of input data and method has an important effect on the clustering result. In addition, our method is helpful for improving the resolution of seismic facies edges and offers the richer depositional information than the traditional clustering methods.