In order to further curb the misuse of Deepfake audio technology, we proposed deep attention mechanism residual network (DAMRN) that can effectively detect forged audio. The network exhibits stable operation, low risk of gradient disappearance and gradient explosion, and high detection accuracy. The structure of proposed network mainly involves the following contents: Firstly, a data balancing strategy is adopted in the front end of the network so that the ratio of positive and negative samples in the data maintained proportional balance, which improves the network performance and reduces the overfitting phenomenon. This strategy has been effectively proven by the experiments in this article. Secondly, we compare the accuracy rates of different depths among the network models for Deepfake audio detection (DFAD), and select the network that best suits the depth of this article. Finally, we introduce an effective attention mechanism in the network structure appropriately to increase the network's sensitivity to forged speech artifact information. By obtaining the artifact information of the Deepfake audio, the network model can learn more falsification frequency features that can effectively distinguish between spoofed and bonafide audio, and the accuracy has been improved to 99.81%, with the EER reduced to 0.69%, compared to the baseline system. Experiments are conducted using three acoustic features (MFCC, LFCC, GFCC) extracted from two mainstream datasets (ASVspoof2019LA, ASVspoof2021DF) respectively, and the results show that the best EER value of the method proposed in this paper is 0.32%, which is a better performance compared with other mainstream models.