<p>In cross-domain sentiment classification tasks, transferring knowledge from a single source domain is often insufficient to achieve the desired performance in the target domain. It is necessary to integrate knowledge from multiple source domains for guidance. This paper addresses the problem of domain generalization and integration under multi-source domain conditions and proposes a multi-source cross-domain sentiment classification method based on generative adversarial learning and transfer learning. We constructed a framework consisting of a feature extractor and a domain discriminator, which learns domain-invariant feature representations across multiple source domains and the target domain through adversarial training and performs data augmentation for the target domain by generating data. The framework consists of four main steps: generative model training, discriminative model training, adversarial training, and transfer learning. We train source domain sentiment classifiers using labeled data from source domains and achieve domain adaptation between source domains and the target domain by alternating the training of the generative and discriminative models, generating pseudo-labeled target domain data. We conducted comparative experiments with various methods on four multi-source domain adaptation tasks using the Amazon review dataset, all of which achieved excellent results, including a significant accuracy improvement in the task of transferring to the kitchen domain, which has a large domain gap. Experimental results show that by minimizing domain differences between source and target domains, our framework successfully improves the performance of the target task and the generalization of the model in multi-source cross domain sentiment classification tasks.</p>