Traditional collaborative filtering recommendation algorithm only bases on matrix.Due to the sparsity of matrix, the quality of recommendation is not high.This paper proposes a hybrid recommendation algorithm whose similarity is combined with the properties of projects.This algorithm improves the data sparseness in collaborative filtering through the change of the weighted factor, controlling the proportion of two kinds of similarity that one is the similarity of attribute between projects and the other is the similarity of item-based collaborative filtering algorithm.And the comprehensive prediction score and user-based collaborative filtering prediction score are combined to improve the quality of recommendation.Finally, the recommendation is given according to the comprehensive scores.Experiments show that the algorithm has better recommendation quality.