Water quality assessment plays an important role in water environmental protection and management.Traditional methods have some limitations in dealing with the uncertainty in assessment and massive information.A Bayesian network model can effectively express and analyze uncertain problems and combine qualitative analysis with quantitative analysis.Based on the mornitoring data for nearly ten years in Xiang-shan harbor of Ningbo City,a Bayesian network model expressing the relationships and interactions between different water quality indexes and water quality levels was constructed by Bayesian network approach.The model structure indicated that ammonia nitrogen,COD,inorganic phosphorus,nitrate,and chlorophyll had direct effects on water quality level,whereas there was an indirect causality between other water quality indexes such as nitrite and inorganic nitrogen and water quality level.The results of the model validation using 200 monitoring data showed that the predictive precision reached 94.8% and the Kappa was 0.892,which suggests the Bayesian network is feasible for assessment and prediction of water quality.