Accurately and quickly identifying the types of natural and unnatural earthquake events is the basic premise of monitoring, prediction, early warning, and other study in the field of seismology, which is of great significance to the prevention, evaluation, emergency rescue, and other work of earthquake disasters. Convolutional neural network model is a representative artificial intelligence deep learning algorithm, which has been widely used in computer vision, natural language processing, object type identification, and other fields in recent years. In this study, AlexNet convolutional neural network model is selected to study the type identification of 1539 earthquake event waveform records in and around Ningxia Hui Autonomous Region, China. Earthquake event waveform records contain three types: natural earthquake, explosion, and collapse, in which both explosion and collapse are unnatural earthquakes. MATLAB software is used to build the training module and test module for AlexNet convolutional neural network model, and the earthquake event waveform record is transformed into an image format file of 224 times 224 pixels as input parameters. Finally, AlexNet convolutional neural network model has the ability of automatic identification of earthquake event types. The results of this study show that the identification accuracy of earthquake event type in training module is 99.97%, the average value of loss function is 0.001, the identification accuracy of earthquake event type in test set is 98.51%, and the average value of loss function is 0.059. After training and testing, 60 different types of earthquake event waveform records were randomly selected, and AlexNet convolutional neural network model was used to identify them automatically. The automatic identification accuracy of natural earthquakes, explosions, and collapses was 90%, 80%, and 85%, respectively. After training AlexNet convolutional neural network model with earthquake event waveform records, it can have accurate and fast automatic identification ability. The accuracy of automatic identification is comparable to that of professional seismic workers, and the time of automatic identification is greatly reduced compared with that of professional seismic workers. This study can provide an implementation idea of deep learning based on artificial intelligence for the identification of earthquake event types and make contributions to the cause of earthquake prevention and disaster reduction.