## 5 Conclusions  
Traditional neighborhood factors are inadequate in learning spatial features. To address these problems, this study takes 21 districts in the main city of Chongqing as an example, and improves the urban dynamic model from two aspects of neighborhood feature learning and non-neighborhood feature learning, respectively, and constructs the RF-SNSCNN-CA urban expansion simulation model. On the one hand, starting from  
Figure 12: Real situation in 2017 and forecast results in 2024 and 2031 for cities in 21 districts of Chongqing. (**a**) Real land use in 2010; (**b**) Real land use in 2017; (**c**) Projected land use in 2024; (**d**) Projected land use in 2031. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **NP** & **PD** & **LPI** & **LSI** & **MESH** & **AI** \\ \hline
2010 & 5499 & 0.1912 & 0.7655 & 101.5996 & 179.0912 & 62.3411 \\
2017 & 8420 & 0.2927 & 1.2015 & 109.1113 & 490.9877 & 71.254 \\
2024 & 6274 & 0.2181 & 1.7121 & 75.3425 & 1107.279 & 83.5528 \\
2031 & 4560 & 0.1585 & 2.2904 & 57.2767 & 2349.293 & 89.1649 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Urban Landscape Index. the improvement of the learning effect of neighborhood features, the learning effect of neighborhood features is optimized by introducing the SNSCNN method to improve the effect and capability of spatial heterogeneity modeling; on the other hand, starting from the improvement of the learning effect of non-neighborhood features, the traditional machine learning model RF is combined with the neighborhood features module of deep learning to improve the simulation accuracy while preserving the global interpretability of the random forest model The main feature of the SNSCNN model is its ability to capture the spatial heterogeneity in the spatial problem by portraying the important change of the driver through channel attention and the differential influence of the neighborhood through spatial attention, so that the interpretability and accuracy of the model are improved, which provides a new way of thinking for studying similar spatio-temporal simulation problems. In terms of the application of the model in this paper, we find that the urban development of the 21 districts of the main city of Chongqing to 2031 in the future is infill agglomeration development, which is consistent with Chinese policies related to the rural revitalization strategy and other related policies. Despite the gradual integration of urban areas, the overall landscape fragmentation of land use increases. There are still some shortcomings in this study, as shown in the following points: (1) The land use types only involve urban and non-urban land. The model can be used in subsequent studies for multiple land class simulations to try to convert multiple land use types into multiple classification task learning. (2) The use of a deep learning approach can at least partially replace the learning of neighborhood features, which is part of the reason why some of the literature ignores the neighborhood factor when deep learning is used to extract development suitability. As to whether it can completely replace the learning of neighborhood features, further experimental verification is needed. (3) At present, in the study of spatial phenomena, there are more evaluation indices for spatial relevance, such as the Moran index, but less for spatial heterogeneity. The evaluation parameters of spatial heterogeneity will be explored in the subsequent research. (4) Due to the uncertainty of policies, it is easy to change the original urban development pattern, and it is difficult to accurately predict the future urban development trend if only based on the historical trend. Scenario prediction can combine historical trends and relevant policies. (5) In this study, we mainly discuss the extraction of development suitability and neighborhood effects in the CA class urban expansion model.