## 1 Introduction
conducted a comparison of land use simulations using 38 machine learning methods and finally found that random forests outperformed most methods overall [27]. In contrast to the methods based on the tuple's own driving factors to extract transformation rules, the neighborhood effect considers the influence of the tuple's neighbors on the central tuple and therefore has been widely used and studied. Many scholars have improved the neighborhood effect to improve the extraction of metacell conversion rules. Since CNNs can be used to extract high-level features of raster data, CNNs and their improved models are widely used to extract neighborhood drivers. [PERSON] et al. first used CNNs to mine the neighborhood features of drivers. [PERSON] argued that previous studies did not sufficiently consider neighborhood effects in the rule mining process, and they used Convolutional Neural Network for United Mining (CNN) in their study. Neural Network for United Mining (UMCNN) to solve this problem [28]. [PERSON] et al. combined multi-labeling with convolutional neural networks and coupled CA models to simulate the complex evolution of land use [29]. [PERSON] et al. proposed the CNN-GRU (Convolutional Neural Network-Gated Recurrent Unit) model for mining the temporal and spatial characteristics of multi-period land use changes [30]. Current land use simulation models portray the patterns of land use change from different perspectives; however, spatial non-smoothness in the process of land use change is a phenomenon that cannot be ignored, and spatial non-smoothness refers to the existence of spatial data heterogeneity at different scales [31]. Spatial non-smoothness has important research value in many fields, such as earth science, ecology, and economics [32; 33; 34]. Geographically weighted regression methods and partitioned modeling methods are typical applications of the strategy of \"seeking common ground while reserving differences\" for spatial non-stationarity in spatial modeling from non-neighborhoods [35; 36; 37]. With the rapid development of big data, deep learning, and other technologies, many new methods have emerged in spatial nonsmoothness modeling in recent years. Some studies have used attention mechanisms to build deep learning models that adapt to the nonsmoothness of spatial data [38]. The attention mechanism can help the model automatically select the input features that need attention, thus improving the accuracy of the model. In simple terms, an attention mechanism is a weighted averaging method that assigns different weights to different input features based on their importance [39].CBAM (Convolutional Block Attention Module) incorporates both channel and spatial attention and can be embedded into a convolutional neural network (Convolutional Neural Network (CNN) [40]. By using CBAM, the network can better focus on the important features, thus improving accuracy and efficiency. [PERSON] et al. found that CNN-CBAM-based simulation models were able to extract features from spatial data more accurately than CNN models with traditional structures [41]. In general, CNN and its coupled model in deep learning are more suitable methods for extracting features of spatial data, but the coupled model of CNN and CBAM has not been applied to the mining of CA-like model neighborhood driving factor features. Based on this, this paper tries to construct an RF-SNSCNN-CA model, that is, on the basis of using the traditional machine learning model RF to obtain the probability of site suitability, the SNSCNN method with the introduction of attention mechanism is used to optimize the learning of neighborhood features to improve the spatial heterogeneity modeling effect in order to better reveal the urban development evolution law. Several sets of comparative experimental scenarios are designed to verify the performance of the RF-SNSCNN-CA model during the model implementation.