Aiming at the limitation of the clustering effect caused by the preference and damping factors in Affinity Propagation (AP), a Teaching and Learning Based Optimization (TLBO) algorithm is proposed. First, the search space of parameter p is determined, and then the TLBO algorithm is used to find the optimal parameter value in the search space. At the same time, the damping factor is automatically adjusted to prevent numerical oscillations during the clustering process, so as to improve the clustering quality of AP algorithm. The experimental results show that the algorithm can effectively solve the problem caused by preference and damping factors, improve the contour coefficient of clustering, and reduce the clustering error rate.