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Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating urban traffic pressure. However, it is highly challenging due to the complexity of the urban geospatial structure and the highly nonlinear temporal and spatial dependence on human mobility. Most existing works proposed to rely on strict periods (e.g. daily and weekly) and separate the extraction of temporal and spatial features. Besides, most Recurrent Neural Network (RNN)-based models either fail to capture variations of spatial–temporal features in adjacent timestamps or ignore details of closeness. In this paper, we propose a M ulti-attention based H ybrid-convolution S patial-temporal R ecurrent N etwork (MHSRN) for region-based traffic flow prediction. In MHSRN, we leverage a hybrid-convolution module to capture both shifting features and rich information at the nearest timestamps, and we apply the downsampling procedure to reduce the computation of RNN-based model. Furthermore, we propose to adopt a space-aware multi-attention module to re-perceive global and local spatial–temporal features. We conduct extensive experiments based on three real-world datasets. The results show that the MHSRN outperforms other challenging baselines by approximately 0.2–8.1% in mean absolute error on all datasets. On datasets other than TaxiBJ, the MHSRN reduces the root mean square error by at least 2.8% compared with the RNN-based model. [ABSTRACT FROM AUTHOR]