Fund Recommendation Algorithm based on AI Model

With the rapid expansion of China’s domestic economy, financial markets have grown increasingly complex, and the variety of fund products has continued to diversify, leaving investors faced with the challenge of difficulty in selecting funds. Traditional fund recommendation methods largely rely on single historical performance indicators, which often fail to provide a comprehensive, objective, and accurate assessment of a fund’s investment value. To address this significant challenge, this study constructs a fund recommendation model based on the random forest algorithm. The model first determines whether a fund is worth purchasing through a classification task, then conducts regression predictions on selected high-quality funds, and finally ranks recommendations based on predicted future returns. During model development, this research integrates multiple dimensions—including historical performance, risk control, fund manager capability, fund size, and fees—selecting 13 key parameters as model inputs. Using a simulated dataset of 2,100 fund samples for training and validation, experimental results show that the random forest classification model achieves 99% accuracy on the validation set, while the regression model attains a mean squared error (MSE) as low as 0.0064, significantly outperforming traditional algorithms such as linear regression and logistic regression. This study offers investors a scientific and effective tool for fund screening and evaluation, with the potential to reduce investor losses and improve economic returns in the future, holding considerable theoretical and practical significance.