Slewing bearing is one of critical transmission in wind turbine and shield machine withstanding low-speed and heavy-load working condition. Fault recognition is crucial to their high reliability operation. Many studies have been conducted using traditional shallow networks for fault recognition. However, they suffer from inherent disadvantages, such as low learning ability under high-dimensional nonlinear features, which make them unsuitable for fault recognition of slewing bearing. To solve these shortcomings, a novel fault recognition method is proposed based on improved deep belief network (DBN) using sampling method of free energy in persistent contrastive divergence (FEPCD). A systematic methodology based on multi-domain feature extraction is proposed to describe the fault characteristic information. After that, improved DBN optimized by FEPCD is employed to capture the fault features and recognize the fault condition of slewing bearing. The application and superiority of proposed methodology are validated using a slewing bearing life-cycle test dataset. Meanwhile, a comparison is conducted between traditional sampling methods contrastive divergence (CD) and persistent contrastive divergence (PCD). The results illustrate that improved FEPCD gets better result in training sampling. Compared with other deep learning methods such as deep Boltzmann machine (DBM) and stacked auto-encoder (SAE), and shallow intelligent algorithms like back propagation (BP) neural network and support vector machine (SVM), the fault recognition accuracy of slewing bearing is improved by using the improved DBN with FEPCD.