A storage and inference method for a deep-learning neural network comprises steps: establishing dummy nodes in a first artificial neural network to form a second artificial neural network; storing model parameters of the second artificial neural network in a first storage area, and storing parameters of the dummy nodes in a second storage area; and in inference, respectively retrieving the model parameters of the second artificial neural network and the parameters of the dummy nodes from the first storage area and the second storage area simultaneously; deleting interconnections between the dummy nodes of the second artificial neural network or setting the interconnections between the dummy nodes of the second artificial neural network to 0 according to the parameters of the dummy nodes before inference. The present invention prevents ANN from be deciphered through respectively storing model parameters and parameters of the dummy nodes in different locations.