An image classification method comprising the following steps: calculating the clarity level of an image; determining that the image belongs to a second quality category recommended to be deleted when the clarity level of the image is smaller than a preset image clarity level, and determining that the image belongs to a first quality category recommended to be saved when the clarity level of the image is greater than or equal to the preset image clarity level; and acquiring all images, successively determining the similarity degree between a current image and a previous image of the current image in the photographing order of all the images, defining the current image and the previous image as images in a same group when the similarity degree is greater than or equal to a preset similarity degree, and defining the current image and the previous image as images in different groups when the similarity degree is smaller than the preset similarity degree; wherein the step of determining the similarity degree between a current image and a previous image of the current image specially comprises: individually dividing the current image and the previous image into a plurality of macroblocks, acquiring RGB values of the macroblocks of each image and converting the RGB values into YUV luma and chrominance components; and individually calculating standard deviation values of the YUV components of corresponding macroblocks in the current image and the previous image, and summing up the YUV standard deviation values of all the macroblocks so as to obtain the similarity degree between the current image and the previous image. calculating the clarity level of an image; determining that the image belongs to a second quality category recommended to be deleted when the clarity level of the image is smaller than a preset image clarity level, and determining that the image belongs to a first quality category recommended to be saved when the clarity level of the image is greater than or equal to the preset image clarity level; and acquiring all images, successively determining the similarity degree between a current image and a previous image of the current image in the photographing order of all the images, defining the current image and the previous image as images in a same group when the similarity degree is greater than or equal to a preset similarity degree, and defining the current image and the previous image as images in different groups when the similarity degree is smaller than the preset similarity degree; wherein the step of determining the similarity degree between a current image and a previous image of the current image specially comprises: individually dividing the current image and the previous image into a plurality of macroblocks, acquiring RGB values of the macroblocks of each image and converting the RGB values into YUV luma and chrominance components; and individually calculating standard deviation values of the YUV components of corresponding macroblocks in the current image and the previous image, and summing up the YUV standard deviation values of all the macroblocks so as to obtain the similarity degree between the current image and the previous image. individually dividing the current image and the previous image into a plurality of macroblocks, acquiring RGB values of the macroblocks of each image and converting the RGB values into YUV luma and chrominance components; and individually calculating standard deviation values of the YUV components of corresponding macroblocks in the current image and the previous image, and summing up the YUV standard deviation values of all the macroblocks so as to obtain the similarity degree between the current image and the previous image.