Aming at the problem that the match algorithms of feature points under complex background have low reliability and weak robustness, a match the feature points algorithm is based on Speed Up Robust Features, namely determine the main direction by calculating the gradient information around feature points using Haar wavelet, generate 64-dimensional descriptors by Gaussian weighted in the sub-region, fast index match by Hessian matrix trace, and then match again by the sum of square of match points distance difference (one of similarity measure methods). Finally, using multiple real-time image to analysis and compare the experimental results of SIFT and SURF algorithms, concluded that SIFT and SURF algorithm is effective for change of rotation, scaling, noise and light and impact of a variety of integrated factors. Synthesis results show that, SURF matching algorithm is better than SIFT matching algorithm. SURF matching algorithm can be fast, robust and accurate.