Spectrum sensing under α noise has become a hot topic in recent years. The statistical model of this noise has obvious impulse and trailing characteristics. The signal characteristics are not obvious enough under weak signal conditions. To this end， the maximum generalized correntropy spectrum sensing method based on stochastic resonance is proposed. This method uses the transition of particles in the stochastic resonance model between the two potential wells to transfer part of the energy of alpha noise into the signal to improve the signal output signal-to-noise ratio. The maximum generalized correntropy method is utilized to construct high-order statistics for spectrum sensing， detect the output signal after stochastic resonance and combine conjugate gradient descent method to achieve the optimal objective function. The simulations results demonstrate that the proposed algorithm can effectively improve the detection performance under the condition of low signal-to-noise ratio.