Focused on the problem that Affinity Propagation (AP) clustering algorithm is sensitive to parameter Preference, which is not suitable for sparse data, and has the incorrectly clustered sample points in the clustering results, an algorithm named Adaptive Affinity Propagation clustering based on universal gravitation (GA-AP) was proposed. Firstly, the gravitational search mechanism was introduced into the traditional AP algorithm in order to perform the global optimization to the sample points. Secondly, on the basis of global optimization, the correctly clustered and incorrectly clustered sample points in each cluster were found through the information entropy and Adaptive Boosting (AdaBoost) algorithm, the weights of the sample points were calculated. Each sample point was updated by the corresponding weight, so that the similarity, Preference value, attractiveness and membership degree were updated, and the re-clustering was performed. The above steps were continuously operated until the maximum number of iterations was reached. Through simulation experiments on nine datasets, it can be seen that compared to Affinity Propagation clustering based on Adaptive Attribute Weighting (AFW_AP) algorithm, AP algorithm, K-means clustering (K-means) algorithm and Fuzzy C-Means (FCM) algorithm, the proposed algorithm has the average values of Purity, F-measure and Accuracy (ACC) increased by 0.69, 71.74% and 98.5% respectively at most. Experimental results show that the proposed algorithm reduces the dependence on Preference and improves the clustering effect, especially the accuracy of clustering results for sparse datasets.