Physiological studies have shown that differences in facial activities between depressed patients and normal individuals are manifested in different local facial regions and the durations of these activities are not the same. But most previous works extract features from the entire facial region at a fixed time scale to predict the individual depression level. Thus, they are inadequate in capturing dynamic facial changes. For these reasons, we propose a multi-scale and multi-region facial dynamic representation method to improve the prediction performance. In particular, we firstly use multiple time scales to divide the original long-term video into segments containing different facial regions. Secondly, the segment-level feature is extracted by 3D convolution neural network to characterize the facial activities with different durations in different facial regions. Thirdly, this paper adopts eigen evolution pooling and gradient boosting decision tree to aggregate these segment-level features and select discriminative elements to generate the video-level feature. Finally, the depression level is predicted using support vector regression. Experiments are conducted on AVEC2013 and AVEC2014. The results demonstrate that our method achieves better performance than the previous works.