Recent years have witnessed the widespread use of domain adaptation. Thought having achieved significant performance in different fields, these methods are hungry for a large amount of labeled data, which requires unaffordable cost to meet the data quality and quantity and hinders the further application of deep learning model. Fortunately, domain adaptation, which not only relaxes the I.I.D assumption between the source and the target domain but also uses the labeled source domain data and the unlabeled target domain data simultaneously, is beneficial to achieve a well-generalized model. Among all the domain adaptation setting, multi-source domain adaptation, which takes full advantage of the information of multiple source domains, are more suitable to the real-world application. This study proposes a multi-source domain adaptation method via multi-measure framework and weighted disentangled semantic representation. Motivated from the data generation process in causal view, it is first assumed that the observed samples are controlled by the semantic latent variables and the domain latent variables, and it is further assumed that these variables are independent. As for the extraction of these variables, the duel adversarial training schema is used to extract and disentangle the semantic latent variables and the domain latent variables. As for the multi-domain aggregation, three different domain aggregation strategies are employed to obtain the weighted domain-invariant semantic representation. Finally, the weighted domain-invariant semantic representation is used for classification. Experiment studies not only testify that the proposed method yields state-of-the-art performance on many multi-source domain adaptation benchmark datasets but also validate the robust of the proposed method.