A signal processing method based on a spiking multi-layer neural network, comprising: converting, by a spike signal conversion apparatus, an input information signal into an input spike signal and inputting the input spike signal to the multi-layer neural network; processing, by at least two layers in the multi-layer neural network, the input spike signal to generate an output spike signal; and converting, by the spike signal conversion apparatus, the output spike signal into an output information signal; wherein the processing, by at least two layers in the multi-layer neural network, the input spike signal to generate an output spike signal comprises: determining a first signal F 1 ( t) output by a first neuron in a first layer in the at least two layers; processing the first signal F 1 ( t) by using q orders of synapse weight parameters w q ( t), w q -1 ( t),···, w 1 ( t), to obtain a second signal F 2 ( t), wherein a speed at which an initial function w x + 1 0 t is met by an (x+1) th -order synapse weight parameter of the q orders of synapse weight parameters attenuates with time t is higher than a speed at which an initial function w x 0 t met by an x th -order synapse weight parameter attenuates with the time t, and 1 ≤ x ≤ q-1; and inputting the second signal F 2 ( t) to a second neuron in a second layer in the at least two layers, wherein the second neuron is a next-layer neuron of the first neuron; wherein q is a positive integer equal to 2; characterised in that the first neuron and the second neuron are connected by using a memristor unit, wherein the first signal F 1 ( t) causes a change in a temperature of the memristor, wherein the change in the temperature of the memristor corresponds to a change in the second-order synapse weight parameter of the memristor unit and when the second-order synapse weight parameter exceeds a threshold, a conductivity value of the memristor is induced to permanently change, wherein the change in the conductivity value of the memristor corresponds to a change in the first-order synapse weight parameter of the memristor unit. converting, by a spike signal conversion apparatus, an input information signal into an input spike signal and inputting the input spike signal to the multi-layer neural network; processing, by at least two layers in the multi-layer neural network, the input spike signal to generate an output spike signal; and converting, by the spike signal conversion apparatus, the output spike signal into an output information signal; wherein the processing, by at least two layers in the multi-layer neural network, the input spike signal to generate an output spike signal comprises: determining a first signal F 1 ( t) output by a first neuron in a first layer in the at least two layers; processing the first signal F 1 ( t) by using q orders of synapse weight parameters w q ( t), w q -1 ( t),···, w 1 ( t), to obtain a second signal F 2 ( t), wherein a speed at which an initial function w x + 1 0 t is met by an (x+1) th -order synapse weight parameter of the q orders of synapse weight parameters attenuates with time t is higher than a speed at which an initial function w x 0 t met by an x th -order synapse weight parameter attenuates with the time t, and 1 ≤ x ≤ q-1; and inputting the second signal F 2 ( t) to a second neuron in a second layer in the at least two layers, wherein the second neuron is a next-layer neuron of the first neuron; wherein q is a positive integer equal to 2; characterised in that the first neuron and the second neuron are connected by using a memristor unit, wherein the first signal F 1 ( t) causes a change in a temperature of the memristor, wherein the change in the temperature of the memristor corresponds to a change in the second-order synapse weight parameter of the memristor unit and when the second-order synapse weight parameter exceeds a threshold, a conductivity value of the memristor is induced to permanently change, wherein the change in the conductivity value of the memristor corresponds to a change in the first-order synapse weight parameter of the memristor unit. determining a first signal F 1 ( t) output by a first neuron in a first layer in the at least two layers; processing the first signal F 1 ( t) by using q orders of synapse weight parameters w q ( t), w q -1 ( t),···, w 1 ( t), to obtain a second signal F 2 ( t), wherein a speed at which an initial function w x + 1 0 t is met by an (x+1) th -order synapse weight parameter of the q orders of synapse weight parameters attenuates with time t is higher than a speed at which an initial function w x 0 t met by an x th -order synapse weight parameter attenuates with the time t, and 1 ≤ x ≤ q-1; and inputting the second signal F 2 ( t) to a second neuron in a second layer in the at least two layers, wherein the second neuron is a next-layer neuron of the first neuron; wherein q is a positive integer equal to 2; characterised in that the first neuron and the second neuron are connected by using a memristor unit, wherein the first signal F 1 ( t) causes a change in a temperature of the memristor, wherein the change in the temperature of the memristor corresponds to a change in the second-order synapse weight parameter of the memristor unit and when the second-order synapse weight parameter exceeds a threshold, a conductivity value of the memristor is induced to permanently change, wherein the change in the conductivity value of the memristor corresponds to a change in the first-order synapse weight parameter of the memristor unit.