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In intensive pig farming, the pig house environment is an important factor affecting the health of pigs. However, the joint precise control of multiple environmental factors has always been a common problem in pig house environment control. Therefore, the adaptive Gaussian filtering (AGF) algorithm combined with long short term memory networks (LSTM) was used to predict the environmental factors inside the pig house, providing support for optimizing the control strategy of the pig house environment. By combining the weighted method, the weights of the environmental evaluation indicators inside the pig house were determined, and an evaluation method based on the unknown measurement method was constructed to provide reference for pig house environment control. The proposed method was validated by using measured data from pig houses, and the results showed that compared with the LSTM prediction model, the LSTM prediction model with the AGF optimization algorithm (LSTM-AGF) improved the prediction performance (R2) of ammonia, temperature, relative humidity, and carbon dioxide concentration by 0.33, 0.03, 0.05 and 0.12, respectively. The proposed prediction evaluation method based on the unknown measurement method had a sensitivity (SENS) of 0.215, which was 20.8% higher than that of the traditional fuzzy comprehensive evaluation method. Therefore, the environmental quality evaluation method proposed can provide feasible reference for precise control of the pig house environment.