In this paper, four deep learning network models, i.e., the CNN, ResNet18, Vgg16, and Inception10 models, were used to classify blasting and seismic events, and the dataset used blasting events and natural seismic events recorded by the Fujian station network from 2016 to 2020. The gradient- weighted class activation mapping (Grad- CAM) algorithm was introduced into the four classification models to address the “black box” problem of deep learning network models, and a visualization of each model was obtained. The visualization diagram provides an intuitive view of the model's reliance weights for different waveform features when making classification decisions, thus providing a basis for the model’s interpretability and improving its credibility. Analysis of the visualization diagram shows that the CNN and Vgg16 models with better classification effects rely more on the seismic phase characteristics of seismic waveforms when making decisions and pay less attention to the pre- earthquake and post- earthquake bands. In contrast, the ResNet18 and Inception10 models are insufficiently sensitive to the seismic phase characteristics. The results obtained from a visual analysis of the models through the Grad- CAM algorithm well reflect the classification effect of the models, which is important for improving the models and selecting an appropriate classification model.