城市内部土地利用变化的模拟研究逐渐受到关注，因其在决策制定与政策形成方面具有重要参考价值。尽管以往研究多集中于城市内部尺度模拟方法的开发，但针对驱动城市内部土地利用变化因素的研究仍较为匮乏。城市规划者高度关注城市内部结构的形成机制及其运行规律。为此，本文基于随机森林（RF）算法构建了元胞自动机（CA）模拟模型，旨在模拟多种城市内部土地利用变化情景，并识别不同驱动因素的贡献程度。本研究引入交通区位、环境条件、公共服务设施及人口密度等多类空间变量作为驱动因子，以深化对城市内部土地利用动态演变的理解。该模型以中国广东省惠州市惠城区2000—2010年实际历史土地利用数据进行验证，并基于验证后的模型模拟生成了2015年的多情景城市内部土地利用分布图。同时，采用随机森林算法的袋外（OOB）误差估计方法计算各空间变量的重要性指标（VIMs），进而评估并分析各驱动因素在该区域中的相对重要性。本研究为城市规划者及相关学者提供了详实、有针对性的信息，有助于制定面向不同类型城市内部土地利用的具体规划策略，并支持该地区未来的可持续发展。

Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.