Capturing the light field data of dynamical scenes has been widely adopted in virtual reality, microscopic observations, industrial inspections, and other important observation applications. The significant amount of data from current light-field imaging systems complicates their acquisition, transmission, and processing. In this study, inspired by the compound-eye optical structure of insects and the visual-neural work mechanism of fast fusion perception, we propose a novel dynamic light-field imaging paradigm for a meta-light-field event imaging system to capture meta-light-field events with high-dimensional light-field time-varying attributes, which can significantly enhance the light-field perception and characterization of a scene. By fully leveraging the spatiotemporal correlation between events under the light-field structure, we further propose a spatiotemporal-correlated event packaging algorithm, which affords efficient and robust extraction of meta-light-field events. To demonstrate the effectiveness of our proposed meta-light-field event imaging system, we construct a prototype system and test various experimental applications in different computer vision tasks, such as scene reconstruction, denoising, and trajectory reconstruction. Both simulation and experiments can fully demonstrate the high efficiency and robustness of our proposed meta-light-field event camera in capturing dynamic light-field information.