There is a chain reaction between precipitation patterns and atmospheric greenhouse gases. Understanding the impact mechanism of the spatiotemporal dynamics of soil greenhouse gases under precipitation changes is of great significance, allowing for a more accurate assessment of soil greenhouse gas budgets under future precipitation patterns. In view of this, the research uses sensors to collect environmental sample data and gas concentration data, using Conv-LSTM to achieve data analysis. The research also introduces the kernel DM model to optimize the gas distribution modeling problem. Compared to manual periodic monitoring or gas monitoring using a single mobile robot, the gas distribution model used in this study is innovative. The innovation lies in its ability to capture global gas flow trends in data sampling and predictive analysis. The results show that when soil moisture changes between 5% and 35%, the soil carbon dioxide gas flux after the water addition treatment takes a 20% soil moisture level as the inflection point, showing a trend of first increasing, and then decreasing. This indicates that the mathematical model proposed in this study is effective in collecting and analyzing environmental data.