Existing research on sequential ad recommendations mainly focuses on user preferences for ads, insufficiently considering positive relationships between ads. Starting from the associations between ads, incorporates both ad networks and user networks into consideration, a multi-round social advertising influence maximization model based on triggering model was constructed. An ad edge based greedy strategy based on multi-round reverse influence sampling was proposed to enhance platform revenue, with theoretical proofs of its strict lower bound guarantee. Experiments showed that compared to existing optimal methods, this method increased the average ad propagation influence revenue by 35%, significantly enhancing ad recommendation effectiveness, providing a new solution for ad sequence recommendations.