In practical engineering, the rotating machinery fault data set is prone to noise labels due to human labeling or data preprocessing, which could lead to the degradation of fault diagnosis model performance. Therefore, the attentive feature Mixup method of rotating machinery fault diagnosis is proposed. First, a residual neural network ( ResNet) is established to extract time-frequency features from the samples. The correct label sample groups, partially noisy label sample groups, and noisy label sample groups are constructed through random grouping and feature interaction. Secondly, an attention mechanism is introduced to calculate the correlation between samples within each sample group, and assign weights to each group of samples. The different weights that can distinguish noisy label samples within partially noisy sample groups are achieved. Then, Mixup is performed on each group of samples according to their weights, which can interpolate noisy label samples and update the attention layer parameters during backpropagation to reduce the proportion of noisy label samples. Finally, the online label smoothing (OLS) is used to update the model′s prediction information by reducing the negative impact of noisy label samples on the model loss update and further suppressing the effects of noisy label sample groups. Experiments on the rotating machinery fault dataset with label noise interference show the effectiveness of the proposed method.