## 1 Introduction  
The land use/cover change (LUCC) models can not only strengthen the understanding of driving factors and key processes behind dynamic change through quantitative means, but also provide support for decision-makers to make land use/cover policies under different development needs through scenario analysis [1]. At present, LUCC model has become a powerful tool to study the process of LUCC and support the preparation of territorial spatial planning. Among the many LUCC models, the Cellular Automata (CA) model has become the mainstream model of LUCC simulation because of its powerful spatial and temporal modeling ability [2]. The CA model changes the cellular state through local simple rules, resulting in macro LUCC results. The CA model is mainly composed of four elements: space, cell, neighborhood effect, and transformation rules [3]. In the traditional CA model, the space is composed of two-dimensional grid space; cell is a unit in a two-dimensional grid; the state of the cell represents the land use type; and the probability of cell transformation into other land use types is determined by neighborhood effect and transformation rules [4]. Neighborhood effect is a function that reflects the interaction between the current cell and the neighborhood cell in the CA model [5]. The transformation rules measure the possibility of cell state transition under the influence of each spatial variable. The core problem in the CA model is how to effectively extract the land conversion rules. In previous studies, many scholars have proposed the method of machine learning to obtain the conversion rules of the urban CA model, such as the logistic regression method [6; 7; 8; 9], ant colony algorithm [10], genetic algorithm [11], simulated annealing algorithm [12], random forest (RF) algorithm [5; 13; 14], artificial neural network algorithm [15; 16], convolutional neural network algorithm [17; 18; 19], and so on. Among them, the RF algorithm has the advantages of high accuracy, moderate time complexity, strong anti-overfitting ability, and elimination of multicollinearity between driving factors. It can also give the LUCC model interpretability through the important measurement of driving factors. The coupling model of the RF algorithm and CA has been verified to not only obtain better simulation accuracy but also help to reveal the mechanism of complex LUCC. There are complex neighborhood interactions in LUCC [20]. In previous studies, statistical data were used as neighborhood driving factors to describe the neighborhood relationship of a place, such as neighborhood density [21], extended neighborhood enrichment [22], spatial autocorrelation factor [23], etc. However, these simple spatial statistical methods do not have a strong ability to capture the spatial characteristics of land use/cover data and cannot mine the time dimension characteristics [24]. LUCC simulation is essentially a time series modeling problem, and the process of LUCC will be affected by historical land use/cover patterns [18]. The convolutional neural network (CNN) model in the deep learning model uses a convolution kernel to extract image information, which can fully consider the neighborhood information of each pixel in the image [25]. Time series data prediction models, such as the Recurrent Neural Network (RNN) model, Long Short-Term Memory (LSTM) model, and Gated Recurrent Unit (GRU) model can mine historical trends from long-term series data [26]. Many scholars combine these two models to extract spatiotemporal features in land use/cover data. [PERSON] et al. (2022) used the neighborhood spatial features extracted by the CNN model as the input of the LSTM model to fully explore the intrinsic relationship between historical LCCs [27]. [PERSON] et al. (2022) combined spatial and temporal neighborhood feature learning in the CNN-GRU model to supplement the extraction of spatiotemporal neighborhood features and long-term dependence in LUCC simulation research [28].