In the contemporary integrated media environment, the innovation of enterprise short video operation and management, as well as the strategic utilization of modern network technologies, are pivotal in amplifying an enterprise’s influence among users on burgeoning integrated media platforms. This study delves into refining the existing recommendation algorithms, which are crucial for enhancing user engagement and retention rates in enterprise short video services. By integrating the Vulture Search Algorithm to optimize the Fuzzy C-Means user clustering technique and leveraging user profiles developed from questionnaire surveys, this research pioneers a novel approach for tailoring the short video recommendation processes. The enhanced algorithm effectively addresses research needs aligned with user pReferencesand enhances the Fuzzy C-Means method. The application of this refined recommendation algorithm demonstrated significant improvements in user stickiness, driven by the recommendations’ accuracy, engagement, enjoyment, and novelty. Factor analysis revealed that these attributes account for 89.553% of the total variance, which highlights their significant impact on user engagement. Consequently, enterprises seeking to innovate their short video operations within the integrated media landscape must prioritize optimizing video recommendation systems to foster user attachment and satisfaction.