To solve the problem of data sparsity in collaborative filtering, this paper proposes a hybrid recommendation algorithm combining multi-source information clustering and IRC-RBM. Firstly, this algorithm takes user trust and project time weight as the clustering basis, uses the K-means clustering algorithm of minimum spanning tree to carry out clustering analysis on users, generates K similar user sets, and conducts scoring prediction on the basis of clustering analysis. Finally, the scoring matrix after clustering and the scoring matrix generated by IRC-RBM model are weighted and fused by linear weighting, and Top-N is used for recommendation. Experimental results show that the proposed hybrid recommendation algorithm significantly improves the accuracy in comparison to the traditional recommendation algorithm.