An image processing device comprising: a voice collector configured to collect a voice signal input by users for an image to be processed; an instruction converter configured to recognize the voice signal and to convert the voice signal into an image processing instruction according to a target voice instruction conversion model; and an image processor configured to process a target area of the image to be processed according to the image processing instruction and a target image processing model, wherein the instruction converter is configured to convert the voice signal via text information into the image processing instruction through voice recognition technology, a natural language processing technology and the target voice instruction conversion model, or to convert the voice signal directly into the image processing instruction through the voice recognition technology, a semantic understanding technology based on the natural language processing technology, and the target voice instruction conversion model, and wherein the instruction converter is configured to divide the image to be processed into areas according to granularity of semantic areas in the image processing instruction and according to the results of applying image recognition technology, to obtain the target area based on the image processing instruction, characterized in that the instruction converter of the image processing device is an artificial neural network chip, which performs an adaptive training on the target voice instruction conversion model prior to collecting the voice signal, which includes the instruction converter to: convert a voice signal into a prediction instruction according to the target voice instruction conversion model, determine correlation coefficients between the prediction instruction and a corresponding instruction set, and optimize the target voice instruction conversion model according to the correlation coefficients between the prediction instruction and the corresponding instruction set, by adaptively updating parameters in the target voice instruction conversion model, wherein the parameters include weights and biases. a voice collector configured to collect a voice signal input by users for an image to be processed; an instruction converter configured to recognize the voice signal and to convert the voice signal into an image processing instruction according to a target voice instruction conversion model; and an image processor configured to process a target area of the image to be processed according to the image processing instruction and a target image processing model, wherein the instruction converter is configured to convert the voice signal via text information into the image processing instruction through voice recognition technology, a natural language processing technology and the target voice instruction conversion model, or to convert the voice signal directly into the image processing instruction through the voice recognition technology, a semantic understanding technology based on the natural language processing technology, and the target voice instruction conversion model, and to convert the voice signal via text information into the image processing instruction through voice recognition technology, a natural language processing technology and the target voice instruction conversion model, or to convert the voice signal directly into the image processing instruction through the voice recognition technology, a semantic understanding technology based on the natural language processing technology, and the target voice instruction conversion model, and wherein the instruction converter is configured to divide the image to be processed into areas according to granularity of semantic areas in the image processing instruction and according to the results of applying image recognition technology, to obtain the target area based on the image processing instruction, characterized in that the instruction converter of the image processing device is an artificial neural network chip, which performs an adaptive training on the target voice instruction conversion model prior to collecting the voice signal, which includes the instruction converter to: convert a voice signal into a prediction instruction according to the target voice instruction conversion model, determine correlation coefficients between the prediction instruction and a corresponding instruction set, and optimize the target voice instruction conversion model according to the correlation coefficients between the prediction instruction and the corresponding instruction set, by adaptively updating parameters in the target voice instruction conversion model, wherein the parameters include weights and biases. convert a voice signal into a prediction instruction according to the target voice instruction conversion model, determine correlation coefficients between the prediction instruction and a corresponding instruction set, and optimize the target voice instruction conversion model according to the correlation coefficients between the prediction instruction and the corresponding instruction set, by adaptively updating parameters in the target voice instruction conversion model, wherein the parameters include weights and biases.