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Accurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon (a-Si) is crucial for accelerating technology development. However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. We resolve phonon vibrational modes of a-Si at a lateral resolution of 20 nm by tip-enhanced Raman spectroscopy (TERS). To project the high dimensional TERS imaging to a low dimensional (i.e. 2D) manifold space and categorize a-Si structure, we developed a multiresolution manifold learning (MML) algorithm. It allows for quantifying average Si-Si distortion angle and the strain free energy at nanoscale without a human-specified threshold. The MML multiresolution feature allows for distilling local defects of ultra-low abundance (< 0.3%), presenting a new Raman mode at finer resolution grids. This work promises a general paradigm of resolving nanoscale structural heterogeneity and updating domain knowledge for highly disordered materials.