In this study, we aimed to develop a new optical biopsy technique for aganglionosis of Hirschsprung disease (HSCR) and we then evaluated a custom designed Raman optical biopsy system combined with deep learning based on convolutional neural networks (CNNs). Surgical specimens of formalin-fixed tissue of HSCR patients were subjected to this study. In the result, we achieved more than 90% classification accuracy between the normal and the lesion segments in mucosa. This study shows that CNN is useful for discriminating Raman spectra of the human gastrointestinal wall.