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In order to determine the current fault behavior of petroleum rotating machinery according to the modal signal frequency level, and realize the accurate diagnosis of mechanical equipment fault behavior, the application of digital twin model with fusion GA optimization algorithm in the diagnosis of petroleum rotating machinery is studied. The optimization rules of GA algorithm are defined, and on this basis, the digital twin model is established, and then the relevant fault behavior data are combined to complete the clustering operation of the oil rotating machinery operating data, and the oil rotating machinery operating data cluster processing based on the digital twin model is realized. The loss of operating data is calculated, the core diagnostic information is recoupled together by means of modal decomposition of descriptive samples, the obtained hyperparameter index is solved jointly, and the specific training mode of data samples is defined to realize the diagnosis of petroleum rotating machinery. The experimental results show that the application of the above diagnosis method belongs to the standard frequency value range of the signal for each type of fault behavior mode signal, which meets the application demand of 100% accurate diagnosis of mechanical fault behavior, and has outstanding feasibility in accurately diagnosing the fault behavior of petroleum rotating machinery.