## 4 Discussion  
This paper optimizes the model from two aspects: spatio-temporal feature mining of neighborhood factors and dynamic iteration of modules. First of all, to solve the problem that the spatio-temporal feature learning of neighborhood factors in traditional machine learning CA model is insufficient, this paper introduces CNN-LSTM to learn the spatiotemporal feature of neighborhood factors of all land use types, and inputs them into RF-CA model as one kind of driver factors with other drivers of the model, so as to improve the learning effect of traditional CA model. Secondly, to solve the problem of low efficiency of random seed selection in traditional CA dynamic iteration, a high-quality seed selection algorithm is used to improve the precision of multi-category land-use dynamic change model and the efficiency of iterative algorithm. In this paper, taking the main urban area of Chongqing as an example, our proposal model, RF-CNN-LSTM-CA, is used to simulate the land use distribution pattern in 2020 from the base year of 2015. In this process, several different neighborhood modeling schemes are designed and the current mainstream model, such as PLUS and FLUS, are compared. The results show that: (1) using the CNN-LSTM model to extract the spatial and temporal characteristics of each neighborhood from the historical multi-period and multi-category land use data as the driving factors can improve the accuracy of the overall model simulation results; compared with the case without considering any static neighborhood drivers, the FOM are improved by by 1.56% and 18.88% for 2015-2020 and 2010-2020. It shows that in the complex simulation of land use change with multi-land-use types, the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type** & **Cropland** & **Grass** & **Forest** & **Shrub** & **Impervious Surfaces** & **Water Area** & **Total** \\ \hline Land use/cover in 2025 & 2,280,545 & 14,458 & 1,204,355 & 32,001 & 245,669 & 62,369 & 3,839,397 \\ Expansion from 2020 to 2025 & 4312 & 591 & 8462 & 783 & 30,370 & 1056 & 45,574 \\ Reduction from 2020 to 2025 & 40,294 & 0 & 4889 & 0 & 0 & 391 & 45,574 \\ \hline \hline \end{tabular}  
* Suppose that the current discussion of the listing is cultivated land. Expansion indicates that it is not cultivated land in 2020, but will become cultivated land in 2025; reduction is the opposite.  
\end{table} 
Table 7: Prediction results and analysis of land use/cover in 2025. Figure 10: Simulation results of land use/cover pattern in 2025 (**a**) and multi-land use expansion from 2020 to 2025 (**b**). simulation accuracy of the model can be improved to a certain extent by using the deep learning model to mine the spatial and temporal characteristics of the neighborhood of each category. (2) The CNN-LSTM model and LSTM model are, respectively, incorporated into the RF-CA model. Through the ablation experiment, it can be seen that LSTM model has advantages in acquiring the temporal characteristics of land use types in historical periods, while the CNN model has advantages in acquiring the spatial characteristics of land use types. Our proposal model in this paper, through the introduction of the CNN-LSTM model, overcomes the shortcomings of the traditional neighborhood that the spatio-temporal feature learning of the neighborhood factor is insufficient, and gives full play to the powerful spatio-temporal feature mining ability of the deep learning model. (3) Compared with the traditional random seed selection algorithm, the high-quality seed selection mechanism can help CA model find the position of seed pixel faster and better, and achieve the conversion to the target land use type, so as to improve the accuracy of spatial simulation and save the running time of the model. (4) This study uses the RF-CA model to decompose the multi-classification problem into several binary-classification problems to build a model to simulate the urban dynamic simulation model.