Deep learning has achieved good success in cardiac MRI. However, these methods are all based on big data, and only deal with single-channel imaging. In this paper, we propose an unsupervised deep learning method for parallel MR cardiac imaging via time interleaved sampling. Specifically, a set of full-encoded reference data were built by merging the data from adjacent time frames, and used to train a network for reconstructing each coil image separately. Finally, coil images were combined via another CNN. The validation on in vivo data show that our method can achieve improved reconstruction compared with other competing methods.