In response to the frequent trip alarm issue in distribution networks, this study innovatively proposes a deep learning-based analysis and prediction method. This method conducts exploratory analysis on massive trip alarm data to reveal the spatiotemporal distribution patterns and influencing factors of events. Subsequently, the ConvLSTM model is employed to automatically extract spatial correlation features of trip events, constructing a high-precision risk prediction model. Based on this, a graph attention network is utilized to model the topology of the distribution network, achieving precise estimation of the impact range and fault propagation paths of trip events. Experimental results demonstrate that this method effectively mines the spatiotemporal correlation information contained in trip data, providing real-time early warning and intelligent decision support for the safe and stable operation of distribution networks. Future research will incorporate more external factors to further enhance model performance and focus on engineering practice applications, providing new ideas and methods for the construction and development of smart distribution networks.