Abstract: Spatiotemporal situations involve the process of change in the continuous spatial distribution of a large number of moving objects over a long period of motion. Aiming at the problems caused by the huge amount of data and unavoidable data loss and distortion for analyzing spatiotemporal situations, a representation learning-based approach is proposed for exploring representative spatiotemporal situations from large datasets. Firstly, we implement a spatiotemporal situation representation model based on the Denoising Autoencoder framework, which can encode each spatiotemporal situation as a compact representation vector. The distance between the representation vectors reflects the overall difference between different spatiotemporal situations. By introducing noise to the input data of the model encoder and allowing the decoder to restore the original data without noise, our model can effectively improve the characterization accuracy and robustness under the conditions of missing and distorted data. Second, we propose a summary algorithm that can visualize the overall characteristics of continuous spatiotemporal situations with a streamlined glyph. Furthermore, we construct a spatiotemporal situation projection for users to quickly find representative spatiotemporal situations from large datasets. Based on the New York taxi dataset and Chicago crime dataset, we use self-similarity and cross-similarity indicators to compare with the existing methods. The quantitative experiments show the representation model's accuracy, robustness, and high execution efficiency under the conditions of large data volume and a high missing rate. User experiments include selection and ranking tasks, and the results illustrate that the representative spatiotemporal situations found by our method are more consistent with users' subjective perceptions.