Synthetic aperture radar (SAR) images provide an important time-series data source for land cover classification. The existing time-series matching algorithms can fully exploit the similarity among time-series features to obtain satisfactory classification results. In this study, the classic time-series matching algorithm named time-weighted dynamic time warping (TWDTW), which comprehensively considers shape similarity and phenological differences, is introduced to guide SAR-based land cover classification. To solve the problem that the traditional TWDTW algorithm only considers the similarity matching of a single feature on the time series, this study proposes a multi-feature fusion-based TWDTW (Mult-TWDTW) algorithm. In the proposed method, three features, namely, the backscattering coefficient, interferometric coherence, and the dual-polarization radar vegetation index (DpRVI), are extracted, and the Mult-TWDTW model is designed by fusing multiple features based on the TWDTW algorithm. To verify the effectiveness of the proposed method, the study implements land cover classification in the Danjiangkou area using time-series data obtained from the Sentinel-1A satellite. Then, the Mult-TWDTW algorithm is compared with the multi-layer perception (MLP), one-dimensional convolutional neural network (1D-CNN), K-means, and support vector machine (SVM) algorithms as well as the TWDTW algorithm using a single feature. The experimental results show that the Mult-TWDTW algorithm obtains the best classification results, manifested as its overall accuracy and Kappa coefficient reaching 95.09% and 91.76, respectively. In summary, the Mult-TWDTW algorithm effectively fuses the information of multiple features and can enhance the potential of time-series matching algorithms in the classification of multiple types of land covers.