## 4 Discussion
At the same time, it uses the deep learning method to obtain the spatio-temporal feature of the neighborhood of each land use type, and uses the neighborhood feature of each land use type as the driver factor of RF, which improves the accuracy of the model and retains the explanatory advantage of the RF model. According to the importance measurement data of driving factors output from the RF model, it can be seen that the influence of driving factors acting on different land use types is different. For the distribution pattern of cultivated land, grassland, and water area, the spatio-temporal feature of their neighborhood factors play the largest role. On the one hand, the test results show the advantages of RF in interpretability through quantitative expression of the importance of driving factors, and also indicate that the important role of neighborhood factors in land use dynamic simulation cannot be ignored. To some extent, this also shows that land use change essentially follows the first law of geography. This research optimizes the model from the aspects of spatio-temporal feature mining and dynamic iteration of neighborhood factors, but it needs further improvement in the future. This study uses the CNN-LSTM model to extract the spatial and temporal characteristics of neighborhood factors of different land use types. Obviously, a more powerful spatiotemporal network model has been developed in the research field of deep learning at present. In the future, we can try to use a better deep learning model to mine the spatiotemporal feature of neighborhood factors. This study also suggests that even data driven models cannot be built without the guidance of basic principles. How to enhance the interpretability of the model in the future land use dynamic modeling includes at least two aspects. One is to further play a role in the quantitative description of factor influence through the use of traditional machine learning models, and the other is to consider how to organically combine with mechanism models (such as the basic laws of geography) while giving play to the advantages of machine learning and deep learning models in mining time space characteristics. Conceptualization, M.L. and H.C.; methodology, H.C.; software, H.C.; validation, M.L. and L.Q. ; formal analysis, M.L. and H.C.; investigation, H.C. and L.Q. ; resources, M.L. ; data curation, H.C.; writing--original draft preparation, H.C.; writing--review and editing, M.L. and L.Q. ; visualization, H.C.; supervision, M.L. ; project administration, C.C. ; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China OF FUNDER grant number 42071218 and Natural Science Foundation of Chongqing, China OF FUNDER grant number cstc2019 cjcyj-msxmX0139. Not applicable. Data Availability Statement: The code and some data of the article can be obtained at [[https://github.com/CQUPT1711](https://github.com/CQUPT1711) laboratory/RF-CNN-LSTM-CA.git]([https://github.com/CQUPT1711](https://github.com/CQUPT1711) laboratory/RF-CNN-LSTM-CA.git). We gratefully acknowledge the support of various foundations. The authors declare no conflict of interest.