Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of evidence on hierarchical organization of functional brain networks.This paper introduces the Hybrid Spatiotemporal Deep Learning (HSDL), by jointly using Deep Belief Networks (DBN) and Deep LASSO to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project (HCP) 900 fMRI datasets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBN, which are then treated as the hierarchical dictionaries for Deep LASSO to identify the corresponding hierarchical spatial maps.Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge.Our proposed novel hybrid deep model is employed to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain.