In order to more accurately determine whether cavitation occurs in the model hydroturbine, an intelligent identification method for turbine polymorphic images is proposed based on image recognition. This method extracts features from the target turbine runner images through machine preprocessing and binarization, and constructs the target feature matrix of the target image. The target feature matrix is multiplied by the correction value obtained from the expert database experience and is input into the cavitation identification model. It is then compared with the template correction feature matrix of the template images stored in the model to achieve cavitation identification of the water turbine runner. The practical application results of the project show that the identification accuracy of this method is about 80%, with a slight occurrence of false positives, but it can meet the requirements for practical use. Compared with existing methods, this method can not only improve the speed of turbine cavitation identification, but also enhance the identification quality, thereby achieving intelligent, mathematical, and simplified turbine cavitation detection.