During the collection process of electroencephalogram （EEG） signals for motor imagery， the subjects’ lack of concentration and failure to strictly follow instructions for corresponding motor imagery result in EEG data that does not match the instructions （labels）， leading to the emergence of “noisy labels”. The presence of “noisy labels” reduces the model’s ability to capture key features and affects the model’s generalization on new subjects. Therefore， this paper proposes a method for motor imagery classification under “noisy labels” condition using multi-scale spatio-temporal feature learning. Firstly， a convolutional neural network is used to extract multi-scale local temporal features from EEG signals， reducing the impact of inter-subject variability. Secondly， feature maps are partitioned in spatio-temporal dimensions and served as input to the Transformer module， with a spatio-temporal feature fusion module used to optimize global spatio-temporal features. Finally， symmetric cross entropy loss is introduced， extending the calculation of cross entropy to all categories to reduce the impact of “noisy labels”. Experimental results on the PhysioNet and BCI IV 2a motor imagery datasets demonstrate that the average accuracy of the proposed method is superior to those of other methods. On the PhysioNet dataset， the introduction of symmetric cross entropy loss improves the average accuracy for two-， three-， and four-class classifications by 0.09%， 0.65%， and 0.66%， respectively. Moreover， symmetric cross entropy loss can improve the model’s classification performance and robustness under different proportions of “noisy labels” interference without increasing the model’s parameter quantity and computational complexity.