【Objective】Constructing a scientific and quantitative quality-assessment model for cultivated land is important for understanding cultivated land quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of cultivated land resources. To accurately reflect on the systematic, complex, and differential characteristics of cultivated land quality, this study aimed to explore an intelligent cultivated land quality assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. 【Method】In this study, taking Xiangzhou in Hubei Province of China as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and the ecological environment—to build a comprehensive assessment index system for cultivated land quality. A total of 1, 590 representative cultivated land quality samples in Xiangzhou were selected, of which 1, 110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight(EW), back propagation neural network(BPNN), and random forest(RF)were selected for training, and the assessment results of cultivated land quality were output through simulations to compare the assessment accuracy of the three methods to verify the reliability and superiority of the RF model. In addition, the distribution pattern of cultivated land quality grades in Xiangzhou in 2018 was also analyzed in this study. 【Result】 The results are summarized as follows: (1)The overall quality of cultivated land in Xiangzhou was better, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grades conformed to a positive distribution trend. From the distribution point of view, the spatial distribution of cultivated land quality in Xiangzhou was unbalanced, influenced by the topography and socioeconomic development level and showing an obvious geographical differentiation pattern, with overall characteristics of high in the north-central area and low in the southern area. The distribution of cultivated land quality grades also varied widely among towns.(2)The RF model for cultivated land quality assessment required fewer parameters and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to cultivated land quality scientifically.(3)In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW.(4)Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination(R²)was 0.8145, the mean absolute error(MAE)was 0.009, and the mean squared error(MSE)was 0.012. 【Conclusion】 The findings in this study showed that RF was more suitable for the quality assessment of cultivated land with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of cultivated land quality assessment at the county scale, and provides a theoretical basis for achieving a threefold production pattern of cultivated land quantity, quality, and ecology in Xiangzhou, while also serving as a reference for the evaluation of cultivated land quality in similar regions.