In order to reduce redundancy of detecting extreme point and various adverse effects of image change factors during using SIFT vehicle logo recognition algorithms. An improved SIFT algorithm based on edge constraint and global structure was proposed, it took advantages of the image moment invariant theory and the image edge detection algorithm to only detect edge regions of target image, eliminating extreme points that have nothing to do with vehicle logo recognition regions, and it divided each feature point neighborhood into circular regions and calculated the maximum curvature of pixel in each group of concentric circles that obtained by the division to construct the global SIFT combination feature vectors, which made the SIFT descriptors had a global describing nature. It also combined the SVM model such that a feature vector classifier of vehicle logo image was created to classify features and recognize vehicle logos. The simulation experiment data indicates that the improved SIFT vehicle logo recognition algorithm may reduce redundant extreme points by about 25 to 45 percent, which enhances the effectiveness of detecting extreme points, and make the average recognition rate reach more than 97 percent, which improves the real-time trait of recognition. It can be seen that higher recognition rate and faster recognition speed can be obained in comparison with several common image feature extraction operators.