An item authentication method, comprising the following steps: acquiring a plurality of images taken on an item to be authenticated, each of the images contains at least one target authentication point, wherein, the target authentication point refers to an element of the item to be authenticated; acquiring at least one single-point score corresponding to each of a plurality of target authentication points through identifying the plurality of images by a plurality of trained convolutional neural network models respectively, each of the trained convolutional neural network models is corresponding to the at least one target authentication point, and, for a given target authentication point, more than one trained convolutional neural network model is used to identify it; characterised in , obtaining a total score through performing a weighted summation process on single-point scores of the plurality of target authentication points according to weights, wherein, the weight is set for each convolutional neural network model according to the accuracy of identifying the corresponding target authentication point by the convolutional neural network model during training of the convolutional neural network model; and authenticating the authenticity of the item through comparing the single-point scores and the total score with a first threshold and a second threshold; wherein the item authentication method outputs an authentication result of fake which indicates the item is fake when the single-point scores are determined to be less than the first threshold; and outputs an authentication result of fake which indicates the item is fake when the total score is determined to be less than the second threshold if the single-point scores are determined to be larger than the first threshold. acquiring a plurality of images taken on an item to be authenticated, each of the images contains at least one target authentication point, wherein, the target authentication point refers to an element of the item to be authenticated; acquiring at least one single-point score corresponding to each of a plurality of target authentication points through identifying the plurality of images by a plurality of trained convolutional neural network models respectively, each of the trained convolutional neural network models is corresponding to the at least one target authentication point, and, for a given target authentication point, more than one trained convolutional neural network model is used to identify it; characterised in , obtaining a total score through performing a weighted summation process on single-point scores of the plurality of target authentication points according to weights, wherein, the weight is set for each convolutional neural network model according to the accuracy of identifying the corresponding target authentication point by the convolutional neural network model during training of the convolutional neural network model; and authenticating the authenticity of the item through comparing the single-point scores and the total score with a first threshold and a second threshold; wherein the item authentication method outputs an authentication result of fake which indicates the item is fake when the single-point scores are determined to be less than the first threshold; and outputs an authentication result of fake which indicates the item is fake when the total score is determined to be less than the second threshold if the single-point scores are determined to be larger than the first threshold. <EOS>