Existing models still face multiple challenges in Aspect Sentiment Quad Prediction （ASQP） task. They have difficulty in dealing with implicit sentiment expressions （such as implicit aspects or opinions） that lack explicit lexical cues， making it difficult for models to accurately capture sentiment tendencies. A quad prediction is considered correct only when all predicted elements of this prediction exactly match the correct elements. However， models may generate easily confused synonyms or synonymous words， leading to completely incorrect quad predictions. Moreover， existing models focus on improving the probability of predicting correct words， ignoring the suppression of easily confused words. Additionally， the cross-entropy loss used by these models makes them overconfident about incorrect predictions， lacking uncertainty modeling and thus failing in actively suppressing high-risk errors. These problems limit the performance of existing models in Aspect-Based Sentiment Analysis （ABSA） tasks. To address these problems， a Supervised Contrastive generative sentiment analysis method with Uncertainty-Aware Unlikelihood Learning （SCUAUL） was proposed. Firstly， supervised contrastive learning was used to shorten the semantic space distance of similar samples （e.g.， same sentiment polarity） through contrastive loss， enhancing the model's ability to distinguish key features （e.g.， sentiment polarity， implicit aspects） of input data. Secondly， Monte Carlo Dropout （MC Dropout） was used to capture the model's inherent uncertainty and identify easily confused words. By marginalizing unlikely learning， the generation probability of easily confused words was dynamically suppressed while maintaining the probability of generating correct words， and a minimum entropy constraint was combined to balance generation diversity and accuracy. Average results of five experiments on the Rest15 and Rest16 datasets showed that， compared with the suboptimal model AugABSA （data Augmentation by text generation for ABSA） and the classic model PARAPHRASE， SCUAUL improved precision by 0.40， 3.98 and 0.38， 3.83 percentage points， the recall by 0.30， 2.87 and 0.48， 2.88 percentage points， and the F1 score by 0.35， 3.43 and 0.42， 3.37 percentage points， respectively， verifying the effectiveness of SCUAUL in ABSA tasks.