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Because the spacecraft works in the harsh environment such as high temperature and high pressure, the autonomy of traditional fault detection methods is relatively poor, and the lack of analysis of fault characteristics leads to low detection accuracy. A spacecraft fault detection technology based on deep learning and GPU calculation is proposed. According to the principle of analysis and detection of spacecraft fault signal characteristics, the GPU image is obtained with the support of GPU computing technology, and the calculation method is introduced into the depth confidence network model. According to the built deep confidence network model, the fault location of bearings is predicted. The fault features extracted by GPU computing technology are used for the basic data of deep confidence network fault prediction. The original data are normalized and the fault characteristics of spacecraft bearings are analyzed. With the support of different parameters, the deep learning algorithm is used to automatically confirm the fault location of bearings. By defining the key parameters of the network, bearing faults can be identified, and fault features can be learned to realize spacecraft fault detection. The experimental results show that the detection accuracy of this technology can reach 98%, and it has strong robustness.