Sunspot area is an important feature to measure the solar activities.Prediction of sunspot area can provide useful information for solar activities and space weather studies,etc.In this paper,we propose a smoothed monthly mean sunspot area prediction method by using an artificial neural network.The prediction model is built by training the area data before the twentieth solar cycle,and then it is used to forecast the data after the twenty-first solar cycle.We also consider the influence of different training steps and prediction steps respectively.The proposed method is able to exactly forecast the sunspot area of the next month,and the relative errors for different training steps are all less than 5%.However,the relative error will get larger if the prediction time is longer.