Using tools of information technology for modeling the forecast of epidemic diseases, especially the appearance of brown plant hopers (BPHs) as well as the variety of BPHs density is very important nowadays. Determining epidemic disease levels in present time and forecasting how epidemic disease levels will happen in the future is a practical and useful thing. This will be helpful for agricultural specialists and farmers to know in advance to prevent and protect rice fields from BPHs actively and effectively. The relation between BPHs' appearance and environmental effects is an advanced science anp technology approach which has been applied by specialists. In this article, the authors suggest a new method for evaluating the interaction of environmental factors that change temporally. This method is to combine Bayesian networks and Markov chain for forecasting BPH epidemic disease levels.