Soil oxygen content (SOC) is one of the important soil environmental factors that affect crop growth. It has the characteristics of time series, instability and nonlinearity. It can accurately predict the change trend of oxygen content in the soil environment, which is helpful to formulate a more reasonable soil aeration and oxygenation program. A prediction model based on the sparrow search algorithm (SSA) and long and short-term memory (LSTM) neural network was proposed, the meteorological environment and soil environment record data during the corn planting period were to recorded by using the equipment at the National Soil Quality Zhanjiang Observation and Experimental Station. The SSA-LSTM model predicted and analyzed the SOC changes, and it was compared with the traditional BP prediction model, LSTM prediction model, GA-LSTM prediction model and PSO-LSTM prediction model. The test results showed that the correlation between SOC and rainfall, soil water content, soil temperature and air-filled porosity was extremely significant, the correlation coefficient was higher than 0.8, the correlation with atmospheric temperature and wind speed was significant, and the correlation with atmospheric humidity and soil respiration rate was relatively significant. The prediction accuracy of the SSA-LSTM model was significantly higher than that of the other four groups of control prediction models. The R2 reached 0.95979, the RMSE was only 0.4917%, the MAPE was 3.7331%, and the MAE was 0.3620%. The degree of fit between the predicted value and the experimental value was high. The research result can provide theoretical support and scientific basis for the accurate prediction of soil oxygen content changes and the application and promotion of soil aeration and oxygenation technology.