Aspect-based sentiment analysis (ABSA) represents a crucial field of natural language processing (NLP). It focuses on deriving detailed sentiment insights from textual content. Dialogue-level aspect-based sentiment quadruple extraction (DiaASQ) is specifically concerned with pinpointing target-aspect-opinion-emotion quadruples within conversations. DiaASQ is important in industries like e-commerce, social media analytics, and customer feedback. However, Current ABSA approaches predominantly focus on single-text scenarios, often overlooking the complexities involved in sentiment analysis within conversational contexts. To fill this gap, this paper presents the IFusionQuad model, which is specifically designed for the DiaASQ task. Our contributions include the innovative integration of CloBlock in ABSA, enhancing feature representation with context-aware weights. The InteractiveNet Fusion Module further advances dialogue understanding by aggregating dialogue-specific features such as threads, speakers, and replies. Components such as CloBlock, gating mechanism, and Biaffine attention effectively mitigate data noise issues, improving the relevance of feature extraction. Empirical evaluation on standard datasets demonstrates that the IFusionQuad model outperforms baseline methods, achieving substantial improvements in quadruple extraction. Specifically, our model shows a 6.59% increase in micro F1 and a 7.05% increase in identification F1 for Chinese datasets, and a 2.65% and 4.69% increase in micro F1 and identification F1, respectively, for English datasets. The results clearly demonstrate our IFusionQuad model’s efficacy, which consistently outperforms baseline models across all evaluation datasets on the DiaASQ task.