Abstract: With the development of economic globalization, the exchange of talents among cities has become increasingly frequent. Brain drain and brain gain have had a tremendous impact on the development of technology and the economy. An in-depth study of the regularities of talent mobility is the basis for the monitoring of talent exchange and the formulation of a scientific talent flow policy. To this end, in this paper, we propose a data-driven talent mobility analysis method to study the patterns of talent exchange among cities and to forecast the future mobility. Specifically, we leverage a data structure named talent mobility matrix sequence, to represent and mine the temporal-spatial patterns of inter-regional talent mobility. The comparison of attractiveness for talents among different cities is analyzed based on the talent flows. Further, we propose a talent flow prediction model based on the combination of both convolution and recurrent neural networks to forecast regional talent flows. Theoretically, the model can alleviate the data sparsity problem as well as reduce the scale of parameters compared with traditional regression models. The model was validated by a large scale of data collected from an online professional network. Experimental results show that the proposed model reduces the error by 15% on average compared with benchmark models.