The invention relates to a tide predicting method for the tide is influenced by various factors, including cyclical factors, such as tidal generation force, and non-cyclical factors, such as wind power, atmospheric pressure, coast characteristics, rainfall, dip angles of the lunar orbit and the like. The predicting accuracy of the traditional harmonic analysis method is influenced by partial tide number, and the traditional harmonic analysis method cannot analyze the influence of non-cyclical factors; the artificial neural network method developed recent years overcomes the defect that the non-cyclical factors cannot be predicted by the harmonic analysis method to a certain extent, but has great data volume required by study training samples and wide involve range, can cover various possible conditions, but has less station historical data of non-cyclical factors. The invention provides a predict model, wherein factors which influence tide non-cyclically, such as wind directions, rainfall, storm surge, coast characteristics and the like, can be fused into the model, and small sample data can receive more accurate results. In the method, a support vector machine (SVM)-based predict model is established, wherein, an SVM toolbox is imported into MATLAB 7.8; training sample data is trained by utilizing svmtrain function; the formed model is tested by using a test sample svmpredict function; and the trained and tested data can predict the tide in the same tide test station.