A computer processing method, comprising: quantifying weights and input neurons respectively, and determining a weight dictionary, a weight codebook, a neuron dictionary, and a neuron codebook; determining a computational codebook according to the weight codebook and the neuron codebook; wherein the quantifying the weights includes: grouping the weights, performing a clustering operation on the weights of each group by adopting a clustering algorithm to divide the weights of each group into m types, where m may be a positive integer and the weights of each type corresponding to a weight index, and determining the weight dictionary, where the weight dictionary includes weight positions and weight indices, and the weight positions refer to positions of the weights in a neural network structure; and replacing all the weights of each type with a central weight, and determining the weight codebook, where the weight codebook includes the weight indices and the central weights; decoding an instruction, so as to determine lookup control information; and looking up output neurons in the computational codebook according to the lookup control information, the weights, the weight dictionary, the neuron dictionary, and the input neurons; characterized in that the grouping the weights includes: a layer-type-based grouping wherein weights of all convolutional layers, weights of all fully connected layers, and weights of all LSTM network layers in the neural network are divided into a group respectively; or an interlayer grouping wherein weights of one or more convolutional layers, weights of one or more fully connected layers, and weights of one or more LSTM network layers in the neural network are divided into a group respectively. quantifying weights and input neurons respectively, and determining a weight dictionary, a weight codebook, a neuron dictionary, and a neuron codebook; determining a computational codebook according to the weight codebook and the neuron codebook; wherein the quantifying the weights includes: grouping the weights, performing a clustering operation on the weights of each group by adopting a clustering algorithm to divide the weights of each group into m types, where m may be a positive integer and the weights of each type corresponding to a weight index, and determining the weight dictionary, where the weight dictionary includes weight positions and weight indices, and the weight positions refer to positions of the weights in a neural network structure; and replacing all the weights of each type with a central weight, and determining the weight codebook, where the weight codebook includes the weight indices and the central weights; grouping the weights, performing a clustering operation on the weights of each group by adopting a clustering algorithm to divide the weights of each group into m types, where m may be a positive integer and the weights of each type corresponding to a weight index, and determining the weight dictionary, where the weight dictionary includes weight positions and weight indices, and the weight positions refer to positions of the weights in a neural network structure; and replacing all the weights of each type with a central weight, and determining the weight codebook, where the weight codebook includes the weight indices and the central weights; decoding an instruction, so as to determine lookup control information; and looking up output neurons in the computational codebook according to the lookup control information, the weights, the weight dictionary, the neuron dictionary, and the input neurons; characterized in that the grouping the weights includes: a layer-type-based grouping wherein weights of all convolutional layers, weights of all fully connected layers, and weights of all LSTM network layers in the neural network are divided into a group respectively; or an interlayer grouping wherein weights of one or more convolutional layers, weights of one or more fully connected layers, and weights of one or more LSTM network layers in the neural network are divided into a group respectively. a layer-type-based grouping wherein weights of all convolutional layers, weights of all fully connected layers, and weights of all LSTM network layers in the neural network are divided into a group respectively; or an interlayer grouping wherein weights of one or more convolutional layers, weights of one or more fully connected layers, and weights of one or more LSTM network layers in the neural network are divided into a group respectively.