Abstract: Floating ice is easy to formed an ice jam in front of flat sluice of channel water delivery during the ice-affected seasons, which will affect the efficiency and safe operation of the channel in severe cases. Here, an experimental for free outflow of flat sluice in the open channel were studied based on indoor physical model test. In order to judge the condition of ice float in front of gates of the water conveyance channel. A discriminant model of ice accumulation and entrainment in front of gate based on Principal Components Analysis-Support Vector Machine(PCA-SVM) algorithm is proposed. The correlation analysis method is used to determine the information correlation between input features, and then the PCA method is used to reduce the dimension of the feature vectors. The aim is to improve the computational performance of the model. The first principal component with a contribution rate of 86% and the second principal component with a contribution rate of 7% were extracted as input features. The optimal parameters of Polynomial kernel function (POL), Gaussian Radial basis kernel function (RBF) and Sigmoid kernel function (SIG) were determined by grid search method. The optimal kernel function was determined as RBF by confusion matrix, and the optimal kernel function parameters C were 137 and \gamma were 0.37. The PCA-SVM model was used for supervised learning of the experimental data. It is found that the Fr_1 and Fr_2 are the main influencing factors of ice entrainment or jam in front of gates, and the H/e and H_1/H are the secondary influencing factors. Furthermore, the established model was applied to the identify the floating ice state in front of the inverted siphon. The aim is to verify the classification performance of the developed model. The results are of the great value for the dispatching management and safe operation of water delivery channels during ice period.