Aiming at the problems of cold start, sparse data, low scalability and low recommendation accuracy caused by insufficient consideration of the correlation between different community clusters, we propose a recommendation algorithm based on the trust of experts in the same community cluster and the trust of experts in different community clusters. In improving the similarity calculation, the improved algorithm not only combines Jaccard correlation coefficient, average score factor of users and Pearson correlation coefficient of weighted processing, but also combines the popularity used to punish the proportion of hot items. When improving the score prediction, the improved algorithm introduces the trust of experts in the same community cluster in the traditional clustering recommendation algorithm, and also introduces the trust of experts in different community clusters. Experiments on the MovieLens dataset show that the improved algorithm not only alleviates the problems of cold start and data sparseness, but also significantly improves recommendation accuracy.