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Forecasting the crowd flows of some densely populated areas (functional areas) at urban level is vital for the city and traffic management and much efforts have been put into this field. However, considering the complex dependencies of crowd flows prediction, the current models generally neglected the multi-scale correlation of spatial view and weakened the influence of external features on crowd flows data. In this paper, we proposed a deep learning framework, called MV-RANet, to deal with them and further improve the prediction performance. Firstly, a double-branch residual attention network was utilized to model the spatial correlation between fine-grained regions and coarse-grained functional areas separately with different branch. And furthermore, some human mobility pattern information were incorporated in MV-RANet to capture the correlation between functional areas in a different view. And as for the impact of external factors, we combined them with crowd flows data by multi-channel mechanism, which can make the external factors and spatio-temporal crowd flows data one-to-one mapping, to refine their influence on the crowd flows. We evaluated our model with two different datasets, a large scale Call Detail Records (CDRs) data and Beijing taxicabs' trajectories data (TaxiBJ) (Zhang et al., 2017). The experimental results demonstrate that the proposed MV-RANet consistently outperforms all the baselines. [ABSTRACT FROM AUTHOR]