Since the small initial variation amplitude of soft fault leads to the low detection efficiency of fault detection algorithm based on traditional neural network observer, a soft fault detection algorithm for Flapping Wing Micro Aerial Vehicle (FWMAV) based on multistep neural network observer and adaptive threshold was proposed. Firstly, a multistep prediction observer model was constructed, and the time-delay ability of it can prevent the observer from being polluted by faulty data. Secondly, the window width of the multistep observer was tested and analyzed according to the actual flight data of FWMAV. Thirdly, an adaptive threshold strategy was proposed to perform the fault detection of the observer residuals with the assistance of residual chi-square detection algorithm. Finally, the proposed algorithm was verified and analyzed with the use of actual flight data of FWMAV. Experimental results show that compared with the fault detection algorithm based on traditional neural network observer, the proposed algorithm has the soft fault detection speed increased by 737.5%, and the soft fault detection accuracy increased by 96.1%. It can be seen that the proposed algorithm can effectively improve the soft fault detection speed and accuracy of FWMAV.