Items detection and recognition have become one of hotspots in the field of computer vision research. Based on image features method has the advantage of low amount of information, fast running speed, high precision, and SIFT algorithm is one of them. But traditional SIFI algorithm have large amount of calculation data and spend long time to compute in terms of items recognition. Therefore, this paper come up with a method of items recognition based on SURF. This article elaborates the basic principle of SURF algorithm that firstly use SURF algorithm to extract feature points of item image, secondly adopt Euclidean distance method to find corresponding interest points of image, and finally get the image after items recognition combination with mapping relation of item image using RANSAC(Random Sample Consesus). Experimental results show that the system of item recognition based on SURF algorithm have better effect on matching recognition, higher instantaneity, better robustness.