In order to improve the management level and processing efficiency of government affairs,we propose a multi-algorithm-combined model to evaluate the replies to public consultations in government service.First,we define the five aspects of evaluation including the reply length,similarity,completeness,interpretability and timeliness,and evaluate the text from four perspectives of content,format,reasonableness,and timeliness.Second,we analyze the types of replies by regression analysis.Then,grade the replies by clustering algorithms of K-means,DBSCAN,Meanshift,and HC clustering.Comparison shows that K-means clustering outperforms the other three algorithms in clustering performance,thus it is combined with regression analysis to evaluate the replies.Finally,the replies to public consultations are graded into 6 categories.The proposed model integrates machine learning including data mining and data analysis into "smart government affairs",and provides a quantitative analysis tool to evaluate the performance of government affairs management.