The sea cucumber Apostichopus japonicus is one of the China’s most prized seafoods, its geographical origin is a decisive factor for its economic value. To quickly and effectively identify the geographical origin of this species, ATR-FTIR spectroscopy coupled with deep learning methods was applied in the present study. Compared to conventional machine learning models (support vector machine, random forest and lightGBM), the proposed one-dimensional convolutional neural network (1D-CNN) model demonstrates significant advantages in terms of data automation and accuracy. The model can correctly classify sea cucumbers from different sea regions up to 90.7%, among which 100% identification of Apostichopus japonicus from the East China Sea can be realized. The results proved that ATR-FTIR spectroscopy combined with 1D-CNN could be used as a rapid and effective technique for tracing the geographical origin of Apostichopus japonicus.