The timely and accurate diagnosis of severe faults in the high-speed train air compressor is crucial due to the potential for significant safety issues. In response to this problem, this paper proposes a high-speed train air compressor fault diagnosis method based on an improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) and t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm.