Taking the Naoli River basin as the research area, three periods of land use data from 2000, 2010 and 2020 were selected to analyze the spatiotemporal changes of land use from two aspects: land use dynamics and transfer matrix. The CLUE-S model was used to simulate and verify the land use change pattern in the study area in 2030. Three scenarios, namely baseline, agricultural development, and ecological protection, were used to predict the land use change pattern in 2030. Finally, the adaptability of cultivated land under these three scenarios was evaluated. The results indicated that the land use type in the study area was mainly cultivated land. From 2000 to 2020, forests, wetlands, water bodies and artificial surfaces showed an increasing trend, while cultivated land and grassland showed a decreasing trend. From 2000 to 2020, land use transfer mainly occurred between cultivated land, grassland and forests. From 2000 to 2010, the grassland area transferred most, followed by cultivated land and forests, with the opposite area transferred out. From 2010 to 2020, the area of forest transfer in and out was the highest, followed by grassland and cultivated land. The CLUE-S model had good simulation ability for land use change in the Naoli River basin, with Kappa coefficient of 0.894 and overall simulation accuracy of 91.18%. Under the baseline scenario, the area of cultivated land, grassland and water bodies was decreased, while the area of other land types was increased. In the context of agricultural development, the area of cultivated land was increased by 23.68%, and the artificial surface area was not changed, while the area of other land types was relatively small. The ecological protection scenario was exactly the opposite to the agricultural development scenario. According to the evaluation index system and the evaluation model, it was calculated that the cultivated land located in the suitable area accounted for more than 96% of the total area, and the unsuitable area accounted for less than 4%. The results can provide scientific decisionmaking for cultivated land quality construction and management in the future.