An identity information comparison method based on a fundus image, wherein the method comprises: recognizing a fundus image by using a neural network to obtain a multi-dimensional feature vector representing the identity of a user; comparing the obtained multi-dimensional feature vector with each prestored multi-dimensional feature vector in a database; and determining, according to the comparison result, whether there is a prestored multi-dimensional feature vector matching a currently obtained multi-dimensional feature vector in the database; wherein the neural network is trained with triple sample data, the triple sample data comprises a first fundus image sample, a second fundus image sample, and a third fundus image sample, the second fundus image sample and the first fundus image sample are fundus images of a same person, and the third fundus image sample and the first fundus image sample are fundus images of different people; in a process of training the neural network, the neural network separately extracts multi-dimensional feature vectors of the first fundus image sample, the second fundus image sample and the third fundus image sample, calculates a first distance between the second fundus image sample and the first fundus image sample and a second distance between the third fundus image sample and the first fundus image sample according to the three extracted multi-dimensional feature vectors, obtains a loss value according to the first distance and the second distance, and adjusts parameters of the neural network according to the loss value; and wherein each fundus image sample used for training the neural network is a fundus blood vessel image in form of binary image, which is obtained by the following steps: dividing a fundus image into a plurality of image patches; utilizing a preset segmentation model to extract features of a blood vessel image in each image patch, so as to obtain a binary image patch with highlighted positions of blood vessels; splicing all of binary image patches to form the fundus blood vessel image used as a fundus image sample for training the neural network. recognizing a fundus image by using a neural network to obtain a multi-dimensional feature vector representing the identity of a user; comparing the obtained multi-dimensional feature vector with each prestored multi-dimensional feature vector in a database; and determining, according to the comparison result, whether there is a prestored multi-dimensional feature vector matching a currently obtained multi-dimensional feature vector in the database; wherein the neural network is trained with triple sample data, the triple sample data comprises a first fundus image sample, a second fundus image sample, and a third fundus image sample, the second fundus image sample and the first fundus image sample are fundus images of a same person, and the third fundus image sample and the first fundus image sample are fundus images of different people; in a process of training the neural network, the neural network separately extracts multi-dimensional feature vectors of the first fundus image sample, the second fundus image sample and the third fundus image sample, calculates a first distance between the second fundus image sample and the first fundus image sample and a second distance between the third fundus image sample and the first fundus image sample according to the three extracted multi-dimensional feature vectors, obtains a loss value according to the first distance and the second distance, and adjusts parameters of the neural network according to the loss value; and wherein each fundus image sample used for training the neural network is a fundus blood vessel image in form of binary image, which is obtained by the following steps: dividing a fundus image into a plurality of image patches; utilizing a preset segmentation model to extract features of a blood vessel image in each image patch, so as to obtain a binary image patch with highlighted positions of blood vessels; splicing all of binary image patches to form the fundus blood vessel image used as a fundus image sample for training the neural network. dividing a fundus image into a plurality of image patches; utilizing a preset segmentation model to extract features of a blood vessel image in each image patch, so as to obtain a binary image patch with highlighted positions of blood vessels; splicing all of binary image patches to form the fundus blood vessel image used as a fundus image sample for training the neural network.