This paper presents a fast and accurate Random Sample Consensus (RANSAC) algorithm based on sampling optimization. Firstly, the prior probability of the matching points is calculated by the similarity measurement of matching points, and the minimum subset for model fitting is selected randomly according to the prior probability, which is tested on all the data, until the suboptimal model is found through iteration. Then, the interior point set corresponding to the suboptimal model is used as the initial set for sampling and the minimum subset of the model is randomly extracted and tested on all the data. If the model is better, then the initial set is updated, and the optimal model is found through iteration. Finally, the optimal model is selected, and the interior point and the final model parameters are obtained. The images with different changes are selected as the experimental data, and the algorithm is tested on both the algorithm operation efficiency and the model precision.The experimental data show that the number of iterations in this algorithm is about 20%, and its running time is about 25% of the standard RANSAC algorithm, and the standard square root error is reduced by about 30%.This paper makes full use of the prior knowledge of the matching point and the results of the model test to optimize the sampling mode, so that the operation efficiency and precision of the algorithm are greatly improved.