Abstract: The Random Forest (RF) regression algorithm is applied to the early cracking resistance research of concrete. Taking Songtong Project Concrete as an example, based on a large amount of literatures and engineering experience, seven early impact resistance index systems of concrete are selected. The training sample set and test set from the original data are established, and the model misjudgment rate is calculated based on the out-of-bag data (OOB) from Bootstrap self-resampling to determine the optimal parameters of the Random Forest Prediction Model and make important factors rank. The Pearson correlation diagram is used to analyze the relevance of various factors, and finally the RF training model is established, and the fitting result of the prediction model training set and prediction set are output.The prediction accuracy of the model is analyzed by the values of RMSE and R², compared with the BP neural network and wavelet neural network models. The results show that the Random Forest Prediction Model has the smallest error and the highest accuracy, furthermore the accuracy and reliability of the model are verified. The proposed Random Forest Prediction Model provides an effective method for realizing the prediction of early crack resistance of concrete.