This article presents the solution of Qufu Normal University for the Multimodal Sentiment Cause Analysis competition in SemEval2024 Task 3.The competition aims to extract emotion-cause pairs from dialogues containing text, audio, and video modalities. To cope with this task, we employ a hybrid pre-train model based approach. Specifically, we first extract and fusion features from dialogues based on BERT, BiLSTM, openSMILE and C3D. Then, we adopt BiLSTM and Transformer to extract the candidate emotion-cause pairs. Finally, we design a filter to identify the correct emotion-cause pairs. The evaluation results show that, we achieve a weighted average F1 score of 0.1786 and an F1 score of 0.1882 on CodaLab.