Focusing on the fact that the order of locations and time play a pivotal role in understanding user mobility patterns for spatio-temporal trajectories, a novel deep representation model for trajectories was proposed. The model considered the characteristics of spatio-temporal trajectories: 1) different orders of locations indicate different user mobility patterns; 2) trajectories tend to be cyclical and change over time. First, two time-ordered locations were combined in location sequence; second, the sequence and its corresponding time bin were combined in the temporal location sequence, which was the basic unit of describing the features of a trajectory; finally, the deep representation model was utilized to train the feature vector for each sequence. To verify the effectiveness of the deep representation model, experiments were designed to apply the temporal location sequence vectors to user mobility patterns mining, and empirical studies were performed on a real check-in dataset of Gowalla. The experimental results confirm that the proposed method is able to discover explicit movement patterns (e.g., working, shopping) and Word2Vec is difficult to discover the valuable patterns.