The Causal Emotion Entailment (CEE) is a sub-task of sentiment analysis field, which aims to discover the cause utterances that trigger speakers’ emotion in a conversation. Current cause utterance recognizing is still unsatisfactory, particularly suffering from long distance between cause utterances and emotion utterances. In this paper, we propose an emotion-cause relation enhanced model (EmoCRT) to better solve the long-distance issue by utilizing four emotion-cause relation types. The experimental results on the RECCON dataset show that the proposed model outperforms the benchmark model by 1.41% in terms of Macro-F1. In addition, we reveal the defects of the large language model (LLM) on this task.