The invention discloses a weight factor-introduced large-scale hydraulic machine intelligent diagnosis method, and relates to the technical field of hydraulic machine fault diagnosis. The method comprises the following steps of firstly, carrying out feature conversion on the original vibration signals by utilizing deep forest multi-granularity scanning, and then substituting the converted featuresinto a cascade forest of the depth forest which introduces weight factors for supervised step-by-step training; finally, obtaining a final classification result by using an extreme gradient lifting method (XGBoost) for result fusion, and completing intelligent diagnosis of a pump station unit. The deep forest model is utilized, so that the complex theoretical analysis and a complex parameter adjusting process of the deep neural network applied in the fault diagnosis field are avoided; and the weight factor is used for correcting the class vectors, so that accuracy and effectiveness of fault diagnosis are improved; by virtue of the extreme gradient lifting method (XGBoost) for result fusion, the intelligentization and the accuracy of the diagnosis are further improved, so that end-to-end intelligent fault diagnosis from the original monitoring data is realized.