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Sheep house humidity has the characteristics of large time delay, nonlinearity and spatial distribution difference, and the interaction mechanism with a variety of environmental parameters is complex and highly coupled. The humidity prediction model constructed by traditional prediction methods is difficult to meet the needs of largescale accurate breeding of mutton sheep. Too high or too low humidity of sheep house will directly threaten the healthy growth of sheep. Timely control of the trend of humidity and early regulation is the key to ensure the welfare of sheep. A nonlinear combined prediction model of sheep house humidity based on singular spectrum analysis (SSA), particle swarm optimization (PSO) and optimized long short-term memory network (LSTM) was proposed for accuracy humidity prediction. Firstly, the normal sequence and noise sequence were separated by SSA, and the original sequence was transformed into smooth sequence. Secondly, the optimal parameter combination of LSTM was determined through PSO iterative optimization to reduce the training cost of LSTM. Finally, a combined prediction model was established according to the optimized parameters to predict the two sequences respectively, and the sum of the model results was the final prediction result. The model was used to predict the air humidity in sheep houses in Xinjiang Uygur Autonomous Region from March 17, 2021 to March 27, 2021. The results showed that the combined prediction model had good generalization, stability and convergence. Compared with the standard ELM, SVR, LSTM, PSO-LSTM,EMD-PSO-LSTM and other models, the proposed SSA-PS-LSTM combined model had higher prediction accuracy. Its mean square error (MSE), mean absolute error (MAE) and determination coefficient (R2) were 1.127%2, 0.803% and 0.988, respectively. The experimental results showed that the established model had better prediction performance, which can provide important decisions for formulating optimized sheep house environmental control strategy, solving the lag problem of environmental control effect, and it made a strong support for the healthy growth of sheep.