The integration of renewable energy sources into power grids may introduce wideband oscillation risks, challenging the stability of modern power systems. Traditional artificial neural network-based data-driven impedance identification methods face significant limitations due to the black-/gray-box characteristics of wind power units (WPUs) and the scarcity of impedance measurement data. To address these challenges, this paper proposes few-shot data-driven online impedance identification and stability assessment for wind-integrated modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems. By enhancing the backpropagation neural network (BPNN) with adversarial domain adaptation (ADA), the proposed online impedance identification leverages transfer learning to develop wideband impedance identification models for WPUs and MMCs, enabling online impedance identification with minimal data requirements and achieving the direct model transfer from WPUs to onshore MMC. A comprehensive case study of the Rudong offshore project in China demonstrates the effectiveness of the proposed few-shot data-driven online impedence identification and stability assessment, showing a 95% reduction in data requirements and significant improvements in model transferability compared with conventional methods.