Existing research works on the influence of social networks mainly focus on the propagation of single-source information, and rarely consider the reverse form of propagation. Aiming at the problem of reverse influence maximization, the heat diffusion model was extended to the multi-source heat diffusion model, and a Pre-Selected Greedy Approximation (PSGA) algorithm was designed. In order to verify the validity of the algorithm, seven representative seed mining methods were selected, and the experiments were carried out on different kinds of social network datasets with the propagation revenue of reverse influence maximization, the running time of the algorithm and the degree of seed enrichment degree as evaluation indexes. The results show that the seeds selected by PSGA algorithm have stronger propagation ability, low intensity, and high stability performance, and have advantage in the early stage of propagation. It can be thought that PSGA algorithm can solve the problem of reverse influence maximization.