[Objective] This paper proposes a method based on recommendation algorithm, portfolio theory and the actual data of China’s online lending market, aiming to help investors make better decisions. [Methods] We collected data from Renren’s Loan Transaction and constructed a bipartite graph network graph for the P2P scenario. Then, we used the recommendation algorithm and Markowitz portfolio theory to choose the investment products. [Results] Under different K values, the accuracy of the improved bipartite graph recommendation algorithm with simple weight were 0.055, 0.044, 0.039, 0.035, 0.036 and 0.032. These results were higher than those of the user-based collaborative filtering algorithms UCF (0.022, 0.019, 0.032, 0.032, 0.033, 0.034) and item-based collaborative filtering algorithms ICF (0.007, 0.013, 0.014, 0.014, 0.014, 0.014). The recall rate was also higher than those of the other two algorithms. [Limitations] The sample dataset needs to be expanded. [Conclusions] Combining recommendation algorithm with group theory could find portfolios with better return of investments.