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Rotation mechanism is more and more widely used in production and life. However, the application environment of rotating machinery is more complex, the production environment is harsh, the components affect each other, and a single signal can not complete the performance of fault characteristics. To solve this problem, a convolutional neural network was constructed according to the attention mechanism, multiple signal sources were used for data extraction, and different signal features were fused to build a rotating machinery fault detection model. The experimental results show that the fault classification accuracy of the constructed model is 99.92%, 1.89% higher than that of the second best algorithm, and the detection accuracy of the data after Fourier transform is improved by 17.32% on average. It can be concluded that the fault detection model constructed can effectively extract and fuse fault features of different data acquisition, which can greatly improve the fault detection accuracy of rotating machinery, and the addition of data feature fusion module to the model can effectively reduce the operating cost of separate calculation and improve the operation speed. It reduces the production safety accidents caused by mechanical failures, can effectively improve product quality and improve the market competitiveness of enterprises.