The support structures of a rotating machine affect its overall dynamic behavior. The stiffness of the support structures often differs at the actual sites compared to the test rigs, which leads to uncertain dynamics. In this research, a novel method is developed to identify the support stiffness for an in-situ machine using a simulation-data-driven, deep learning algorithm. In this transfer learning approach, a deep learning algorithm is trained with a simulation model and then tested with measured vibration of a rotor-bearing-support system. To validate the algorithm for multiple cases, an experimental test rig with variable horizontal support stiffness is used. The results from the deep learning algorithm are compared with Linear regression (LR), Artificial Neural Network (ANN), and Support vector regression (SVR) for benchmarking. The models are trained with filtered frequency domain response, and challenges due to measurement uncertainty are analyzed. With the proposed pre-processing steps of the frequency domain response and outlier elimination, the deep learning-based virtual sensor can predict the support stiffness with reasonable accuracy, where the limiting factor is the data quality and lack of excitation at critical speed frequencies.