With the development of fifth-generation mobile communication technology, a huge volume of mobile data have been generated which enable a wide range of location-based services. As a result, user location prediction has attracted attention from researchers. However, existing methods have low accuracy due to the sparsity of user check-ins. In order to address this issue, we propose a method for user location prediction based on similar living patterns. We first obtain a vector representation of each user’s living habits to cluster users with similar living patterns. Then, embedded vectors of POI category and POI location are learned. Finally, we construct activity prediction model and location prediction model for each user cluster by using Gate Recurrent Unit (GRU). The experimental results for real user check-ins show that the proposed method outperforms the baseline methods in most cases.