Concentrations of ammonia and carbon dioxide are important indicators for indoor environment control in pig house. Due to the time-varying and nonlinear coupling characteristics of gas concentration, the prediction accuracy of pig house environment prediction models is still relatively low. Aiming to achieve the precision control for gases concentration in pig house, a time-series data prediction model named ISSA-GRU-ARIMA for harmful gas concentrations was proposed based on gated recurrent unit (GRU), improved sparrow search algorithm (ISSA) fused with autoregressive integrated moving average model (ARIMA). Firstly, a GRU gas concentration time series prediction model was constructed, and Tent chaotic sequence, chaotic disturbance and Gaussian mutation were introduced to enhance the local optimization ability of ISSA algorithm and optimize the hyperparameters of GRU model;then the statistical learning ARIMA method was used to extract the linear features of the optimized ISSA-GRU model’s prediction residuals in order to improve the prediction accuracy of the model. A dataset with 1248 environment data that collected for 52d was used for model training and testing. It was shown that the RMSE, MAPE and R2 of ISSA-GRU-ARIMA model for ammonia concentration prediction were 0.263mg/m3, 8.171% and 0.928, respectively, and those for carbon dioxide concentration prediction were 55.361mg/m3, 4.633% and 0.985, respectively. The constructed ISSA-GRU-ARIMA had high predictive performance, it can provide scientific basis for accurate control of harmful gases in pig house.