Unlike event causality identification, event causality extraction aims to extract causal event pairs and their structured event information from plain text, a more challenging task that requires models with strong causal inference to capture event causality. However, existing studies do not fully exploit the interactions between causal events. To this end, we propose a financial event causality extraction model based on prompt learning (EBPL), which constructs prompt templates to mine potential causal knowledge in the pre-trained language model, and enhances the semantic interactions between causal event types and event arguments through hybrid semantic fusion to improve the ability to identify causal relationships. The experimental results indicate that EBPL significantly outperforms all baseline models across all metrics on the ECE-CCKS dataset.