The free spread of public opinions may lead to the occurrence of cyber collective behaviors， which are easy to cause negative social impacts and threaten public security. Therefore， the establishment of network public opinion monitoring and early warning mechanism is necessary to prevent and control the spread of public opinions and maintain social stability. Firstly， by analyzing the formation mechanism of rumors， a prediction index system of public opinion development was constructed. Secondly， the multifactor GM（1， n ） model was established to predict the development trend of the public opinion. Then， the prediction model was improved by combining with metabolism theory and Markov theory. Finally， using the “Xinjiang cotton” event and “Chengdu No.49 middle school” event in Weibo as examples， the abilities of the GM（1， n ） model， the Markov GM（1， n ） model and the metabolic Markov GM（1， n ） model to predict the development of public opinions were compared，and the metabolic Markov GM（1， n ） model was also compared with the random forest model.Experimental results show that the average prediction accuracy of the metabolic Markov GM（1， n ） model is increased by 10.6% and 5.8% compared with those of the original GM（1， n ） model and random forest model respectively. It can be seen that the metabolic Markov GM（1， n ） model has good performance in predicting the development trend of network public opinions.