Rapid and accurate classification of earthquake (eq) events is a serious challenge in seismology and disaster mitigation. Problems, such as data imbalance, model interpretability, and model generalization, limit the application of artificial intelligence methods in this research area. This article introduces a real-time two-stage diffusion eq event classification (DiffEEC) model based on the diffusion probability model (DPM). DiffEEC uses a two-stage classification approach and combines DPM and knowledge distillation (KD) techniques. DiffEEC focuses on seismic phase-related features and source mechanism-related features by combining time-series features extracted by convolutional layers, DPM output, and InSAR data features to better extract core seismic data information and reduce reliance on manual feature design. DiffEEC uses focal loss to solve the data imbalance problem. Thus, DiffEEC can address data scarcity and imbalance, feature acquisition and selection, variability and complexity of seismic event processing, and model generalization through the mechanism. Experiments show that DiffEEC performs better in eq event classification (EC).