Abstract: Under the background of the vigorous development of big data, the quantitative prediction of mineral resources is the core part of geological big data. The basic idea of comprehensive analysis and mining of multi-information coincides with the concept of big data. With the Lala copper deposit as the study area, the authors carried out 3D mineral resources prediction based on machine learning. In this paper, 3D geological model was established to extract useful information of mineralization and build the quantitative prediction model of the study area. By using the "cube prediction model" prospecting method, the authors adopted the random forest algorithm of machine learning to calculate the probability distribution of mineralization in the study area. In this way, five prospecting prospective areas were delineated. The results show that the random forest has higher prediction accuracy and stability and can make quantitative evaluation on the importance of ore controlling factors. This study has successfully applied machine learning to the 3D mineral resources prediction and made a positive exploration for the prediction and evaluation of mineral resources in the future.