The invention relates to a method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters. The method is characterized by comprising the following steps of: selecting four different artificial intelligent computation models and taking parameters acquired in an ammonia process desulphurization system operational process such as multiple groups of flue gas amounts, flow of a circulating pump, the flow of a concentration pump, the ammonia concentration, the concentration of absorption liquid, the liquid-gas ratio, the inlet flue gas temperature, the ammonia consumption, the density of spraying slurry, the pH value of slurry of a spraying tower and the pH value of the slurry of a pre-washing tower as input variables of the four models; respectively training each model, and establishing a non-linear function relationship between four desulphurization parameters and the desulphurization efficiency; then respectively transmitting parameters monitored in real time into the trained artificial intelligent model, and predicting the desulphurization efficiency; and taking the average value of two predicted values in the middle as a final predicted value... The method disclosed by the invention can be used for better predicting the ammonia process desulphurization efficiency and has the characteristics of higher stability and stronger prediction capability compared with single model prediction.