Sub-event detection can help faster and deeper understanding of an event by providing human-friendly clusters, and thus has become an important research topic in Web mining and knowledge management. In existing sub-event detection methods, clustering based methods are brittle for using heuristic similarity metric to judge whether documents belong to the same sub-event, while topic model based methods are limited to the bag of words assumption. To overcome these drawbacks in previous research, in this paper, we propose an encoder-memory-decoder framework for sub-event detection. Our model learns document and sub-event representations suitable for the similarity metric in a data-driven manner, and transforms sub-event detection into selecting the most proper sub-event representation that can maximize text reconstruction probability. Considering the case of overfitting, we also apply transfer learning in our model. To the best of our knowledge, our model is the first to develop an unsupervised deep neural model for sub-event detection. We use Twitter as an examplar social media platform for our study, and experimental results show that our model outperforms baseline methods for sub-event detection.