A method for compressing a neural network model implemented by a computer, comprising: acquiring a to-be-compressed neural network model; determining a first bit width, a second bit width and a target thinning rate corresponding to the to-be-compressed neural network model, wherein the first bit width is determined as a vector width of a processor for running the to-be-compressed neural network model, and the second bit width is determined according to a vector width of an instruction set in the processor; obtaining a target value according to the first bit width, the second bit width and the target thinning rate, which comprises: calculating a product between the second bit width and the target thinning rate; and taking a division result between the first bit width and the product as the target value; and compressing by thinning parameters of the to-be-compressed neural network model using the target value, the first bit width and the second bit width to obtain a compression result of the to-be-compressed neural network model, comprising: taking continuous parameters with a number corresponding to the target value in the to-be-compressed neural network model as a parameter unit; sorting the parameters contained in the parameter unit according to an ascending order of absolute values; obtaining a zero setting quantity according to the first bit width, the second bit width and the target value; setting parameters in the parameter unit of the to-be-compressed neural network model before the zero setting quantity to zero to obtain a first neural network model; and obtaining the compression result of the to-be-compressed neural network model according to the first neural network model. acquiring a to-be-compressed neural network model; determining a first bit width, a second bit width and a target thinning rate corresponding to the to-be-compressed neural network model, wherein the first bit width is determined as a vector width of a processor for running the to-be-compressed neural network model, and the second bit width is determined according to a vector width of an instruction set in the processor; obtaining a target value according to the first bit width, the second bit width and the target thinning rate, which comprises: calculating a product between the second bit width and the target thinning rate; and taking a division result between the first bit width and the product as the target value; and compressing by thinning parameters of the to-be-compressed neural network model using the target value, the first bit width and the second bit width to obtain a compression result of the to-be-compressed neural network model, comprising: taking continuous parameters with a number corresponding to the target value in the to-be-compressed neural network model as a parameter unit; sorting the parameters contained in the parameter unit according to an ascending order of absolute values; obtaining a zero setting quantity according to the first bit width, the second bit width and the target value; setting parameters in the parameter unit of the to-be-compressed neural network model before the zero setting quantity to zero to obtain a first neural network model; and obtaining the compression result of the to-be-compressed neural network model according to the first neural network model. taking continuous parameters with a number corresponding to the target value in the to-be-compressed neural network model as a parameter unit; sorting the parameters contained in the parameter unit according to an ascending order of absolute values; obtaining a zero setting quantity according to the first bit width, the second bit width and the target value; setting parameters in the parameter unit of the to-be-compressed neural network model before the zero setting quantity to zero to obtain a first neural network model; and obtaining the compression result of the to-be-compressed neural network model according to the first neural network model.