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As a necessary component in rotating machinery, any failure of rolling bearings may lead to the failure of the machine or even the whole system, which leads to huge economic loss and time wastage. Therefore,it is necessary to diagnose the rolling bearing fault promptly and accurately. In response to the problem that the model parameters in the traditional extreme learning machine have a large influence on the fault diagnosis accuracy of rolling bearings, a rolling bearing fault diagnosis method based on a deep kernel extreme learning machine with Bayesian optimization is proposed. Firstly, the deep kernel extreme learning machine (DKELM) model is constructed by combining the auto encoder (AE) with the kernel extreme learning machine (KELM). Secondly, a Bayesian optimization algorithm is used to find the optimal hyperparameters in the DKELM, such that the sum of the classification error rates of the training and validation datasets in the DKELM model is minimized. The test dataset was then fed into the trained BO-DKELM for fault diagnosis. Finally, the proposed method was validated using the Case Western Reserve University bearing fault dataset, and the final fault diagnosis accuracy is 99.6%, comparing with traditional intelligent algorithms such as deep belief networks and convolutional neural networks, the proposed method has higher fault diagnosis accuracy.