This study proposes an improved collaborative filtering recommendation algorithm integrating expert trust aiming at the data sparsity and cold start in the current algorithms. This algorithm divides users into different community clusters based on the optimization of initial clustering centers in DBSCAN. Considering the influence of user activity on similarity calculation, we introduce the penalty weight of user activity to improve the similarity calculation. After expert selection, the balance factors in projects are introduced, since the expert trust for different projects varies. Thus, each project evaluated has an independent expert trust. Experimental results on the MovieLens data set show that the proposed algorithm can effectively alleviate data sparsity and cold start, increasing the recommendation accuracy.