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The gearbox plays a crucial role in the transmission system of wind turbines, making gearbox fault diagnosis a pivotal issue in wind turbine health management. Considering the spectral complexity of gearbox vibration signals, multipoint optimal minimum entropy deconvolution emerges as a straightforward and effective approach for gearbox fault diagnosis. This method not only eliminates interference such as background noise and vibration interferences but also highlights weak fault-induced? impulse signals. The performance of this method, however, is contingent upon the appropriate selection of the pre-parameter of filter length to a certain extent. Inadequate or excessive filtering may result from an inappropriate choice. In order to address this issue, an adaptive optimal minimum entropy deconvolution method based on the salp swarm algorithm is proposed in this paper, which enables the adaptive selection of the optimal filter length and achieves superior filtering efficacy. The envelope spectrum of the optimal filtered signal is ultimately obtained through the utilization of the envelope demodulation technique, thereby revealing the fault characteristic frequency. The principle and effectiveness of the proposed method are demonstrated through the simulation and experimental analysis of the gearbox signal extracted from a wind turbine test stand.