In order to reduce the ammonia concentration in the cattle house, it is necessary to accurately predict the ammonia concentration in real time. This paper proposes an improved particle swarm QPSO-RBF combined prediction method of ammonia concentration in cattle house based on KPCA nuclear principal component analysis. At first, the collected environmental data is normalized and denoised based on KPCA nuclear principal component analysis, and the non-linear kernel principal component information of the sample information is extracted through the contribution rate. And, the relevant parameters of the radial neural network RBF are optimized based on the improved particle swarm algorithm QPSO global search performance to improve the accuracy and robustness of prediction. The method is applied to the prediction of ammonia concentration in a cattle farm in Langzhong, and the experimental results show that the KPCA-QPSO-RBF ammonia combined prediction model proposed in this paper has higher credibility and accuracy compared with the other four prediction models. The ammonia concentration prediction method proposed in this paper can provide a theoretical basis for the precise environmental regulation of the cattle house.