Aspect Sentiment Triplet Extraction （ASTE） identifies sentiment entities， attributes， and polarities， enabling precise analysis of sentiment associations in user opinions. This supports product and service optimization， public opinion monitoring， and consumer decision-making. To address limitations of traditional methods in modeling long-range semantic dependencies and insufficient target-domain data， a cross-domain data augmentation graph convolutional network model was proposed for ASTE. During cross-domain data augmentation stage， pseudo-label generation technology based on Maximum Mean Discrepancy （MMD） was introduced to label unlabeled target-domain data initially to mitigate domain shift， a domain-adaptive language model was trained to capture target-domain-specific semantics， and autoregressive data generation was performed by this domain-adaptive language model， so as to further expand the target-domain labeled data volume and diversity. During sentiment triplet extraction stage， the data after first-stage augmentation were used to fine-tune the graph convolutional network-based language model in the pseudo-label generation process， then the fine-tuned model was employed for sentiment triplet extraction. Experimental results on the ASTE-DATA-V2 dataset show that the proposed method outperforms baseline methods such as BGCA （Bidirectional Generative Cross-domain ABSA）， FOAL （Fine-grained cOntrAstive Learning）， and HiPM-hard （Hybrid Prompts Mixture）. Compared with the baseline BGCA， the proposed method has the average F1 score increased by 0.81 percentage points， improving the accuracy and stability of cross-domain sentiment triplet extraction.