## 1 Introduction  
Land use change is one of the main research components of global environmental change and sustainable development [1]. Land use change simulation has become an important tool for land use change research with a wide range of applications, including the urbanization process, ecological environmental protection, and agricultural development [2, 3, 4]. The urban sprawl simulation model can be considered a special case of land use simulation model, focusing the simulation object on the urban boundary. At present, there are three main aspects of research on land use change. One is the study of the drivers of land use change [5, 6, 7]. By analyzing a large number of experimental cases, researchers study the drivers of land use change by cutting from the most important factors, such as natural and economic multiple types. Second, the study of land use evolution mechanisms using relevant models to predict land use changes and future trends [8]. In this process, researchers have used a series of models, including statistical models and dynamic models, to better understand the mechanisms and trends of land use change. Third, multi-scenario land use simulation [9; 10; 11]. In this study, researchers used land use change models to simulate future land use based on different scenarios. These scenarios include economic development, population growth, policy changes, etc., and aim to predict the impacts of land use changes on ecosystems, economy, and society and to propose effective management strategies. Currently, land use change simulations mainly simulate regional land use changes by coupling quantitative and spatial prediction models [12]. Among them, quantitative prediction models include logistic regression models, gray prediction models, Markov models, and system dynamics models [13; 14; 15; 16], while spatial prediction models include CA models, PLUS (Patch-generating Land Use Simulation) models, and multi-intelligence models, etc. [17; 18; 19]. Although each of these two models has certain limitations, coupled quantitative and spatial prediction models can predict future land use conditions more accurately. Therefore, more and more scholars tend to adopt this coupled approach for their research. Since the CA model was used for the evolution of complex systems in the 1970s, it has now been widely applied to simulate the spatial and temporal dynamics of land use and has achieved very good results [20]. Compared with other discrete system models, such as the Petri-net model and linear programming model, CA models can generate complex global behaviors and structures through simple local rules and interactions in a \"bottom-up\" manner [21; 22]. The CA model consists of a rule grid in which each cell has discrete states, cells update their states at discrete time steps, and the new state of a cell depends on its own and neighboring cells' current states, as well as on predefined transfer rules [23]. A large number of scholars have introduced some new driving factor feature mining methods to mine the transition rules of the model by improving the traditional CA model. In land use simulation, the metacell conversion rules and land use change drivers are closely related [24], and scholars have used a variety of different algorithms to extract metacell conversion rules in order to fully explore their intrinsic mechanisms. Among the methods for extracting metacell conversion rules, many studies focus on mining the conversion rules by analyzing the driving factors of the metacell itself. [PERSON] et al. used an artificial neural network algorithm coupled with a CA model to predict land use change in Selangor, Malaysia, between 2031-2051 [25]. [PERSON] et al. designed a support vector machine model to deal with the nonlinear relationship between driving factors and land use change and used it for urban sprawl prediction in Guilford County, North Carolina [26]. [PERSON] et al.