The estimation of multiple matching models between wide baseline or large angle images is a quite challenging task in image processing. The existing algorithms can be used to estimate multiple matching models and their inliers between images well, but their results are prone to matching pairs mis-classification issues. In order to accurately estimate the multiple matching models and allocate matching pairs, this paper proposed a image multi-matching model estimation algorithm based on the aggregation of matching points of the same model (AMPSM) . First, for improve the proportion of correct matching pairs, filter out incorrect matching pairs based on the distribution characteristics of correct matching points in the neighboring region. Furthermore, based on the different matching model degrees to which the matching pairs belong, the suspected intersection matching pairs of multiple models, that is interference matching pairs, are searched for. Meantime, for reduce the impact of interference matching pairs on the accuracy of matching classification, they are removed. Afterwards, for improve the clustering degree of matching points with the co-model, the position is dynamically moved based on the distance between the points within the same model and the center of gravity of the point set during the sampling process. Finally, classifying clustered matching points by MeanShift to obtain a multi matching model. In the experiments, the proposed method is compared with classical framework based algorithms RANSAC, PROSAC, MAGSAC++, GMS, AdaLAM, PEARL, MTC, Sequential RANSAC, and deep learning based algorithms SuperGlue, OANet, CLCNet, CONSAC, etc. Results indicate over 30% increase in the inlier rate, 8.39% reduction in the mis-classification rate of multi model estimation. It is concluded that the new algorithm has significant advantages in incorrect matches filtering and multi-model estimation.