This paper addresses the challenges of poor accuracy and reliability in fault diagnosis for hydraulic mechanical drive gear sets by proposing a research approach based on Support Vector Machines (SVM). The study begins by collecting vibration signals from the hydraulic mechanical drive gear group and constructing a fault signal separation model. Utilizing a low-rank algorithm, the research separates the vibration source signals of the hydraulic mechanical drive gearbox. Constraint conditions are designed for gear group fault signals to facilitate their classification. Based on these classification results, the SDAE model is employed to extract fault features from the hydraulic mechanical drive gear group. The extracted features are then input into the SVM for training, with the final output being the optimal diagnostic result. This approach achieves fault diagnosis of the hydraulic mechanical drive gear group based on SVM. Experimental results demonstrate that the classification error rate of this method does not exceed 3.5%, confirming its high feasibility.