At present, semi-supervised classification research of time series mainly focuses on univariate time series, due to the complex relationship between Multivariate Time Series (MTS) variables, there is less research on semi-supervised classification of MTS. In view of this, we proposes a semi-supervised MTS classification method based on Two-Dimensional Singular Value Decomposition (2DSVD), which first computes the eigenvectors of row-row and column-column covariance matrices, and then extracts feature matrices from MTS samples. The number of rows and columns of the feature matrix is not only lower than the original MTS sample, but also clearly considers the two-dimensional nature of the MTS sample. The experimental results on 10 MTS datasets show that the semi-supervised classification performance of this method is significantly better than the method using extended Frobenius norm, center sequence, and based on one dimensional singular value decomposition.