The gears of wind turbines operate under multiple noise and high-speed conditions for a long time, and the vibration signals exhibit nonlinear characteristics, which makes it difficult for single scale filtering methods in morphological filtering to accurately and effectively extract fault features, a method for gear fault feature extraction using the adaptive multi-scale composite differentiation product morphological filter (AMCDPMF) is proposed. Firstly, three morphological differential operators are introduced to construct the muti-scale composite differentiation product morphological filter (MCDPMF). Secondly, an adaptive selection of the optimal structural element (SE) scale for multi-scale filtering signals is achieved using a composite fault feature index. Subsequently, by integrating the MCDPMF with the optimal SE scale, the AMCDPMF filter is formulated to enable adaptive filtering, thereby effectively extracting fault features from simulated and experimental signals of gear dynamic models. Finally, through comparative analysis with single-scale filters and three classical multiscale filters, The energy ratio (ER) and improvement in signal-to-noise ratio (ISNR) of AMCDPMF have increased by 5.15% and 3.5 dB, respectively. The ability to extract gear fault features is better, effectively improving the reliability of wind turbine operation and promoting the efficient utilization of renewable energy.