To improve the accuracy of short video content distribution, the interest tendencies and the personalized demands for short video content of social groups that the users belong to were analyzed, and in the short video application scenarios based on the active recommendation approaches, a short video content distribution strategy was designed with the goal of maximizing the profit of video content providers. Firstly, based on the federated learning, the interest prediction model was trained by using the local album data of the user group, and the user group interest vector prediction algorithm was proposed and the interest vector representation of the user group was obtained. Secondly, using the interest vector as the input, the corresponding short video content distribution strategy was designed in real time based on the Combinatorial Upper Confidence Bound (CUCB) algorithm, so that the long-term profit obtained by the video content providers was maximized. The average profit obtained by the proposed strategy is relatively stable and significantly better than that obtained by the short video distribution strategy only based on CUCB; in terms of total profit of video providers, compared with the Upper Confidence Bound (UCB) strategy and random strategy, the proposed strategy increases by 12% and 30% respectively. Experimental results show that the proposed short video content distribution strategy can effectively improve the accuracy of short video distribution, so as to further increase the profit obtained by video content providers.