Abstract: In order to identify influencing factors for accident severity and to reduce casualties and economic loss in maritime traffic accidents,factors are extracted by developing a database of accidents information based on statistical anal-ysis of maritime accident data from 2015 to 2016.The factors mainly include ship type,accident location,time,gross tonnage of ships,visibility,and wind force,etc.According to the number of casualties and the amount of economic loss caused by maritime accidents,their consequences are divided into three levels,and a three-class model based on support vector machines(SVM)is established.Then cross-validation and a grid search algorithm are used to optimize penalty pa-rameters and kernel function parameters of the SVM model.An optimal classification model is developed.After that, SVM-RFE algorithm is used to calculate the weights of accident severity of the influencing factors.Furthermore,the fac-tors that have the greatest impacts on the consequences are identified.The results indicate that the overall classification accuracy of the three-class SVM model is larger than 70%.Self-sinking,accidents of fishing vessels,and accidents hap-pen during the autumn period are more likely to result in more casualties.Hazardous chemical ships,inland river acci-dents,and fishing vessels tend to have larger economic loss.