<p>Multivariate time series classification finds extensive applications in medical diagnosis, human health monitoring, and other fields. However, existing methods pay insufficient attention to the unique “two-dimensional structure” (variable dimension and time dimension) of multivariate time series, resulting in relatively low classification accuracy. Therefore, this paper proposes a multivariate time series classification model based on a variable-time dual-attention mechanism, which separately extracts features from the variable and temporal dimensions of the sequence to achieve accurate classification. Specifically, the model proposed in this paper primarily consists of two components: a variable-time dual-attention module and a convolutional feature enhancement module. First, the feature enhancement module extracts “state features” at each time step through one-dimensional convolution, and concatenates them with the original input to achieve feature enhancement. Next, the variable-time dual-attention module applies convolutional attention simultaneously across both variable and time dimensions to the enhanced data, extracting key variable features and critical time points while suppressing less significant components. Experimental results on multiple real-world datasets show that the proposed method attains the highest average accuracy of 84.04% among all compared approaches.</p>