The diversity and complexity of linguistic expressions often lead to event coreference relations being reflected as latent correlations between event mentions. Existing methods predominantly rely on semantic similarity computations based on internal event features, such as triggers and arguments, which limits their ability to address such latent correlations effectively. To overcome this limitation, an external knowledge-enhanced event coreference resolution method is proposed. This approach leverages large language models (LLMs) to generate external knowledge related to coreference, encompassing discourse coherence, logical relationships, and common sense background knowledge. First, the ultra-large language model ChatGPT is utilized to construct training data enriched with external knowledge. Next, foundational LLMs like FlanT5 are fine-tuned on this data to acquire the ability to generate coreference-related external knowledge. Finally, the fine-tuned LLM generates document-level event summaries and chain-of-thought (CoT) style coreference reasoning paths. By integrating internal event features with external knowledge, the proposed method effectively identifies event coreference. Experimental results on the KBP dataset demonstrate that the proposed method outperforms previous state-of-the-art baselines.