Setting up a suitable quantitative analysis model is a basic work for scientific grasp of cultivated land utilization efficiency and its distribution pattern, and can provide reasonable decision-making basis for sustainable utilization of cultivated land then realizing the coordinated development of cultivated resources and environment. In order to effectively describe the complexity, dynamics and heterogeneity characteristics of cultivated land use system, a random forest (RF) model for measuring cultivated land utilization efficiency is constructed by applying random sampling Bootstrap to build a classification tree reasonably. Then by taking 172 cities in the major grain producing areas of China as an example, the RF model was trained to measure the cultivated land utilization efficiency in 2003-2015 compared with Back Propagation Neural Network and Entropy weight to verify the consistency, representative and superiority of RF. The results show that: (1) RF model has fewer parameters and simpler implementation. It can simulate the complex relations among the evaluation indexes, which makes it convenient to analyze the value of each index. (2) For efficiency measurement results of the same space unit, RF > BPNN > EW, the overall distribution pattern of the cultivated land utilization efficiency in RF and BPNN is similar while a great difference exists in EW. (3) Judged from the matching degree of evaluation results to reality and the accuracy parameters, the measurement results are reasonable and in accordance with the facts in RF, which reflected its high applicability and reliability. At the same time, compared with the other two commonly used models, RF can reduce the dimensions of input vectors and the computing complexity, then raise the training efficiency. The correlation coefficient R of RF is 0.8685, M_RPD is 2.3533, with the minimum M_MSE and M_MAE being 0.0174 and 0.0211, respectively, which is more suitable for the study of the cultivated land utilization efficiency with complex nonlinear characteristics, and this method has explored a new way for evaluating cultivated land utilization efficiency.