  As an ensemble average result, vibrational spectrum simulation can be
time-consuming with high accuracy methods. We present a machine learning
approach based on the range-corrected deep potential (DPRc) model to improve
computing efficiency. DPRc method divides the system into ``probe region'' and
``solvent region''; ``solvent-solvent'' interactions are not counted in the
neural network. We applied the approach to two systems: formic acid \ch{C=O}
stretching and MeCN \ch{C+N} stretching vibrational frequency shifts in water.
All data sets were prepared using Quantum Vibration Perturbation (QVP)
approach. Effects of different region divisions, one-body correction,
cut-range, and training data size were tested. The model with a single molecule
``probe region'' showed stable accuracy; it ran roughly ten times faster than
regular DP and reduced the training time by about four. The approach is
efficient, easy to apply, and extendable to calculating various spectra.
