Mueller matrix spectroscopic ellipsometry (MMSE) is a nondestructive tool for nanostructure analysis, and recently the enhanced computational power, combining neural networks and simulation data, enhance its analysis ability on more complex geometries. This study introduces a deep learning method to realize fast and accurate analysis; predicting nanostructure parameters by pairing Mueller matrices with relatively limited library data and then applying neural network algorithm. Thus, it was realized to predict the width and height of 1D grating structure with an accuracy of MAE below 0.1 nm through the proposed two-step prediction algorithm. Finally, experimental validation on SiO2 grating of 38 nm width and 100 nm height showed a good agreement in the dimensions with reasonable range compared to those measured by scanning electron microscopy.