With the rapid development of the crowd-funding, the rapid increase in the number of such projects costs investors a lot of time and effort. This paper aims to help investors select high-quality crowd-funding projects with the least time. Under the assumption that there is a positive correlation between the quality of public projects and the completion ratio of financing(Ratio), the model in this paper is modeled using the CART regression tree algorithm based on the JD Crowd-funding data with R² reaching 0.746. The results show that investors should focus on the Target Amount (TA), the Follower, the Progress and the Topic. The results of this paper are only applicable to reward-based crowd-funding projects. For other types of Crowd-funding, the independent variables should be re-selected for model building, but the decision tree model can still be applied.