Abstract: The temperature in edible fungi greenhouse has the characteristics of time-variant, nonlinear and multi-coupling, so accurate and effective temperature predictions can effectively help growers adjust the greenhouse environment and prevent edible fungus production and quality decline. Based on the perspective of mining the time-series information in historical temperature data. This paper described the specific steps to realize the MA-ARIMA-GASVR-based hybrid combination method to predict the temperature in the edible fungus greenhouse. Firstly, we assumed that the historical temperature series data of edible fungus greenhouse was a dynamic combination of linear and non-linear components, the historical temperature sequences were decomposed into linear sequences and residual sequences using the moving averages (MA) method. Then, time series analysis was conducted to established the model of the autoregressive integrated moving average (ARIMA) by using linear sequence after the decomposition of the moving averages, and the future trend of linear sequences was predicted by the established model. Afterward, to better fit the relationship between temperature trends and various noises in the environment, the autoregressive integrated moving average model prediction value, the historical residual data and the historical temperature data were employed as the input of the support vector regression (SVR) model, and the genetic algorithm (GA) was used to optimize the parameters of the support vector regression model to improve its performance, the parameters being optimized are penalty parameter and radial basis function kernel parameters in the support vector regression model. Finally, the hybrid model output was the temperature prediction value which was more in line with the actual situation. Moreover, the hybrid method was verified using the experimental data from the edible fungus greenhouse in Beijing. In this paper, a representative edible fungus greenhouse was selected as the experimental object according to the observation time requirements and the time-varying needs of edible fungus greenhouse temperature, which was located in the Daxing District of Beijing. A total of 2 208 measured edible fungus greenhouse temperature data were collected from July 1st, 2019 to September 30th, 2019 during the experiment. The experimental data acquisition device used the JXBS-7001 temperature monitoring sensor was used to automatically collect and record the experimental data. Three sets of sensors were deployed in the edible fungus greenhouse to record the experimental data set which included the average temperature data. We trained the proposed model by using data from July 3rd, 2019 to July 16th, 2019 and forecasted the temperature of the next two days and compared temperature prediction experiments with different models and different time intervals. The results indicated that the MA-ARIMA-GASVR-based hybrid model could better fit the original temperature data, the mean squared error, the mean absolute error and mean absolute percentage error of an hour interval temperature were 0.18, 0.36, 1.34, and three error evaluation indexes all showed that the prediction accuracy of the hybrid method in this paper was better than the single models of support vector regression and support vector regression optimized by genetic algorithm, and it was also superior to the hybrid methods which were not processed by moving averages method or optimized by genetic algorithm. Besides, the mean squared error, the mean absolute error and mean absolute percentage error of 6hours interval temperature were 0.29, 0.52, 1.95. the hybrid method in this paper can satisfy the multi-step prediction within 6 hours, which could provide more time for edible fungus producers to adjust the temperature in the greenhouse.