In order to solve the problem that the preference of the traditional affinity propagation clustering algorithm is difficult to choose and the number of generate clusters is likely to be overmuch, an affinity propagation clustering method based on the probabilistic undirected graph model is proposed in this paper. Firstly, the probabilistic undirected graph model is constructed for sample data, while the probability density is calculated for each sample data by maximum clique and potential function. Then the probability density as a priori clustering knowledge is put into the preference of the affinity propagation algorithm to improve its efficiency. The clustering accuracy of the algorithm is further promoted by using the Gauss noise reduction and cluster merging method. Experimental results on the UCI data sets show better clustering efficiency and accuracy of the proposed algorithm against several other similar algorithms.