A bovine iris image recognition method based on SIFT feature bag, characterized in that this method includes the following steps: Executing Step 2 if the optimal SIFT feature bag is not detected, otherwise going to Step 10; Obtaining a training image set used to generate the optimal SIFT feature bag; Acquiring an optimal SIFT feature bag based on the training image set; Obtaining a target bovine iris image set; Preprocessing every target bovine iris image; Detecting iris inner boundary for every target bovine iris image; Obtaining SIFT feature points of every target bovine iris image by SIFT method; Removing the SIFT feature points in the region inside the iris inner boundary to obtain valid SIFT feature points of every target bovine iris image; Comparing the SIFT feature points of every target bovine iris image with the optimal SIFT feature bag and obtaining a feature histogram of every target bovine iris image; Receiving the image to be recognized; Preprocessing the image to be recognized; Detecting iris inner boundary for the image to be recognized; Obtaining SIFT feature points of the image to be recognized by SIFT method; Removing the SIFT feature points in the region inside the iris inner boundary to obtain valid SIFT feature points of the image to be recognized; Comparing the SIFT feature points of the image to be recognized with the optimal SIFT feature bag and acquiring a feature histogram of the image to be recognized; Calculating histogram distance between the image to be recognized and every image in the target bovine iris image set; Choosing the object corresponding to the target bovine iris image with the smallest histogram distance as recognition result; Ending; and wherein the step of acquiring an optimal SIFT feature bag includes: Obtaining a training image set; Preprocessing every training bovine iris image; Detecting iris inner boundary for every training bovine iris image; Obtaining SIFT feature points of every training bovine iris image by SIFT method; Removing the SIFT feature points in the region inside the iris inner boundary to obtain an valid SIFT feature point set of the training image; Merging the SIFT feature point sets of all training images to obtain a SIFT feature point space of the training samples; Adopting KNN method to conduct clustering analysis of the SIFT feature point space of the training sample to obtain K categories and the corresponding feature means. Giving every feature mean a tag to identify the category this feature mean stands for and obtain an optimal SIFT feature bag consisting of K feature means with tag; Ending. Executing Step 2 if the optimal SIFT feature bag is not detected, otherwise going to Step 10; Obtaining a training image set used to generate the optimal SIFT feature bag; Acquiring an optimal SIFT feature bag based on the training image set; Obtaining a target bovine iris image set; Preprocessing every target bovine iris image; Detecting iris inner boundary for every target bovine iris image; Obtaining SIFT feature points of every target bovine iris image by SIFT method; Removing the SIFT feature points in the region inside the iris inner boundary to obtain valid SIFT feature points of every target bovine iris image; Comparing the SIFT feature points of every target bovine iris image with the optimal SIFT feature bag and obtaining a feature histogram of every target bovine iris image; Receiving the image to be recognized; Preprocessing the image to be recognized; Detecting iris inner boundary for the image to be recognized; Obtaining SIFT feature points of the image to be recognized by SIFT method; Removing the SIFT feature points in the region inside the iris inner boundary to obtain valid SIFT feature points of the image to be recognized; Comparing the SIFT feature points of the image to be recognized with the optimal SIFT feature bag and acquiring a feature histogram of the image to be recognized; Calculating histogram distance between the image to be recognized and every image in the target bovine iris image set; Choosing the object corresponding to the target bovine iris image with the smallest histogram distance as recognition result; Ending; Obtaining a training image set; Preprocessing every training bovine iris image; Detecting iris inner boundary for every training bovine iris image; Obtaining SIFT feature points of every training bovine iris image by SIFT method; Removing the SIFT feature points in the region inside the iris inner boundary to obtain an valid SIFT feature point set of the training image; Merging the SIFT feature point sets of all training images to obtain a SIFT feature point space of the training samples; Adopting KNN method to conduct clustering analysis of the SIFT feature point space of the training sample to obtain K categories and the corresponding feature means. Giving every feature mean a tag to identify the category this feature mean stands for and obtain an optimal SIFT feature bag consisting of K feature means with tag; Ending.