Aiming to address the insufficient diagnostic accuracy in existing rolling bearing fault diagnosis research, this paper proposes a rolling bearing fault diagnosis algorithm based on optimized intrinsic mode function adaptive noise-assisted ensemble empirical mode decomposition (CEEMDAN) and variational inference. First, the intrinsic mode function components of the original signal are obtained using CEEMDAN. A sensitive intrinsic mode function component screening algorithm is then designed to optimize the CEEMDAN method, generating a feature vector. For the training dataset, a Gaussian mixture model is established. Through variational inference, the Gaussian mixture model approximates the probability distribution of the feature vector to achieve rolling bearing fault diagnosis. The effectiveness of the proposed algorithm is validated through examples. Compared with CEEMDAN combined with variational inference, local feature scale decomposition combined with variational inference, and optimized CEEMDAN combined with particle swarm optimization support vector machine, the diagnostic accuracy is improved by 4.3%, 4.3% and 21.7%, respectively.