It is of great significance to effectively and quickly identify the most influential users in the open source software community, which can be represented by a complex network. Traditional methods of measuring node influence only consider some topology characteristics of the network, so the results are one-sided. Because there are different interactive behaviors among users in the community, it is necessary to mine more interaction information, so as to give a more comprehensive measure of node influence. In view of this, this paper proposes a method to maximize the node influence of open source software community based on a probability propagation model. Firstly, according to the relationship of users and their interactive records on projects, this paper quantifies the feedback of users on the project from three aspects (i.e. approve, save and modify) and establishes a new probability propagation model between users. Secondly, this paper proposes an algorithm(SIUF) to evaluate users' influence in the open source software community based on the probability propagation model. The algorithm fully considers the interaction behavior of users in the community. In the first stage, the user's own activity is taken as the initial ranking. In the second stage, the influence of neighbor nodes is taken into account, and the SIUF value of nodes is accumulated while the rich club effect is weakened. Finally, the proposed theory and method are applied to GitHub, a typical open source software community, and the correctness and effectiveness of this method are verified from influence spread and speed.