In the development of intelligent systems, multi-label event classification plays a vital role in enabling accurate decision-making across diverse scenarios such as incident response, customer service, and urban management. Although existing graph-based approaches for multi-label classification have shown potential, they struggle to model directed label dependencies and lack explainability, resulting in black-box decision-making processes. To address these issues, we propose a novel LLM-enhanced Event Evolutionary graph for Explainable Classification (LE2C) method. Specifically, we first leverage the powerful semantic learning capabilities of Large Language Models to construct an event evolutionary graph that models event dynamics. Furthermore, we introduce a co-occurrence probability matrix to enhance the expressivity and explainability of the graph, guiding explainable classification. Extensive experiments on two large real-world event classification tasks demonstrate the efficiency, effectiveness, and explainability of LE2C. The code is available at https://github.com/NinaLiangjy/LE2C .