Cross-domain aspect-level sentiment analysis (ABSA) uses annotated samples from the source domain to help train aspect-level sentiment analysis tasks on the target domain. However, not all samples from the source domain are suitable for transfer training, and some samples may have negative migration effects on the training of the migration model, which requires sample screening. Existing cross-domain instance transfer methods consider a one-sided migration basis, ignoring the synergistic effect between samples and affecting the cross-domain generalisation performance. In order to solve the problems of lack of domain-specific training samples and sample screening in cross-domain transfer in ABSA tasks, this paper proposes a cross-domain ABSA method based on high-quality sample selection by combining high-reliability machine learning theories and domain adaptation methods in modelling with the open environment of multidomain sentiment analysis. Firstly, the method designs inter-domain and intra-domain high-quality sample selection metrics to filter the source domain data at the domain level and the sample level in turn, which takes into account the advantages of the two sample selection granularities. Secondly, we comprehensively design the similarity metrics between source and target domains, and efficiently calculate them through graph neural network. Finally, the scenarios of multi-source domain migration are included in the discussion of cross-domain ABSA, and the inter-domain joint adaptability scores are designed to select the multi-source domain combinations with high inter-domain synergies by balancing the overlap and difference of domain features. A cross-domain transfer task is designed on a benchmark dataset covering six domains, and experiments are conducted on three sub-tasks of aspect-level sentiment analysis to validate the effectiveness of the proposed method.