To address the problem that most existing algorithms and models for calculating user influence in networks only consider topology and greedy algorithms and rarely take into account the importance of trust degree on node influence, this paper proposed a global trust model (GTM) for evaluating node influence. The trust relationships of a node with its neighbor nodes were calculated as the local trust degrees. Then, the Beta reputation model was used to obtain the global trust degree through the local trust degrees of the node. Finally, the node influence was evaluated according to the global trust degree of the node. Experiments were conducted on real network datasets to compare this model with classical influence algorithms. The experimental results show that the proposed method not only has lower time complexity but also demonstrates a favorable influence propagation ability in addition to ensuring node trustworthiness and accuracy.