A training method of a neural network for implementing image style transfer, comprising:: acquiring a first training input image and a second training input image;: inputting the first training input image to the neural network;: performing a style transfer process on the first training input image by using the neural network, so as to obtain a training output image;: based on the first training input image, the second training input image and the training output image, calculating a loss value of parameters of the neural network through a loss function; and: modifying the parameters of the neural network according to the loss value, in a case where the loss function satisfies a predetermined condition,: obtaining a trained neural network, and in a case where the loss function does not satisfy the predetermined condition, continuing to input the first training input image and the second training input image so as to repeatedly perform the above training process, characterized in that: the loss function comprises a weight-bias-ratio loss function; the neural network comprises a first convolutional neural network, the first convolutional neural network comprises a plurality of first convolutional kernels and a plurality of biases, and the parameters of the neural network comprise the plurality of first convolutional kernels and the plurality of biases, the weight-bias-ratio loss function is expressed as: L L 1 = W B + ε where L L1 represents the weight-bias-ratio loss function, W is an average value of absolute values of the plurality of first convolutional kernels, B is an average value of absolute values of the plurality of biases, and ε is a positive number.: acquiring a first training input image and a second training input image;: inputting the first training input image to the neural network;: performing a style transfer process on the first training input image by using the neural network, so as to obtain a training output image;: based on the first training input image, the second training input image and the training output image, calculating a loss value of parameters of the neural network through a loss function; and: modifying the parameters of the neural network according to the loss value, in a case where the loss function satisfies a predetermined condition,: obtaining a trained neural network, and in a case where the loss function does not satisfy the predetermined condition, continuing to input the first training input image and the second training input image so as to repeatedly perform the above training process, characterized in that: the loss function comprises a weight-bias-ratio loss function; the neural network comprises a first convolutional neural network, the first convolutional neural network comprises a plurality of first convolutional kernels and a plurality of biases, and the parameters of the neural network comprise the plurality of first convolutional kernels and the plurality of biases, the weight-bias-ratio loss function is expressed as: L L 1 = W B + ε where L L1 represents the weight-bias-ratio loss function, W is an average value of absolute values of the plurality of first convolutional kernels, B is an average value of absolute values of the plurality of biases, and ε is a positive number. the loss function comprises a weight-bias-ratio loss function; the neural network comprises a first convolutional neural network, the first convolutional neural network comprises a plurality of first convolutional kernels and a plurality of biases, and the parameters of the neural network comprise the plurality of first convolutional kernels and the plurality of biases, the weight-bias-ratio loss function is expressed as: L L 1 = W B + ε where L L1 represents the weight-bias-ratio loss function, W is an average value of absolute values of the plurality of first convolutional kernels, B is an average value of absolute values of the plurality of biases, and ε is a positive number.