The working environment of emitters is becoming increasingly complex and changeable, which makes the data source complex. Inter - domain specific emitter identification (SEI) encounters the issue of non-consistent distributions among multiple source domains. This situation leads to suboptimal performance of the target domain model. Consequently, this paper presents a multi-source domain adaptation method with dynamic weights (MDADW). Specifically, first, the dynamic weight module measures the inter-domain similarity by using the independence criterion and generates weights. Secondly, dynamic weights are employed to evaluate the significance of multiple source domains. By doing so, it can comprehensively explore the knowledge within each source domain, thus facilitating the enhancement of the recognition accuracy in the target domain. On a multi-source domain WiFi dataset built in a laboratory environment, MDADW achieved a recognition accuracy of 98.4%. In order to check the robustness of the model, Gaussian white noise is added to the original data set. In a noisy environment, MDADW achieved an average recognition accuracy of 84.0%.