This article intercepts the horror audio clips from the network and movies to build terrorism audio dataset. However, the source of the horror audio is limited, whereas the convolutional neural network depends on a large amount of data. To this end, the transfer learning technology is performed in the discrimination of the terrorism audio. Firstly, pre-train the network by using the public TUT acoustic scenes dataset, and then retain the model weight and transfer the neural network to the discrimination of terrorism audio. Finally, add more layers after the fine-tune network to utilize more audio information, the structure of the added layers is similar to the residual network. The experimental results indicate that the average discriminant rate of the transfer learning method is 3.97% higher than that of the non-transfer learning method, which effectively solves the training problem caused by small audio dataset in the study of terrorism audio discrimination, and the average discriminant rate of the improved transfer learning network has increased by 1.01%, finally reaches the discriminant rate of 96.97%.