Dynamic deuterium MR spectroscopic imaging (DMRSI) is a powerful metabolic imaging method, with great potential for tumor imaging. However, current DMRSI applications are limited to low spatiotemporal resolutions due to low sensitivity. This work overcomes this issue using a machine learning-based method. The proposed method integrates subspace modeling with deep learning to effectively use prior information for sensitivity enhancement and thus enables high-resolution dynamic DMRSI. Experimental results have been obtained from rats with and without brain tumor, which demonstrate that we can obtain dynamic metabolic changes with unprecedented spatiotemporal resolutions.