To establish a method applicable for online monitoring of adulteration of corn oil with penony seed oil in the market, this paper studied the potential of portable 785 Raman spectroscopy technology for quality inspection in the first-line market and prepared 525 corn oil adulterated peony seed oil products. The stability of the portable near-infrared Raman spectroscopy data set of each kind of mixed oil and the differences of Raman spectrum peaks of 21 types of mixed oil were evaluated. The vibration attribution of Raman spectrum peaks was analyzed. The Raman spectral data sets of 16 kinds of oil mixtures were randomly selected to train the quantitative model, and the Raman spectral data sets of the remaining 5 kinds of oil mixtures were used to test the quantitative model. Significant difference existed between the near-infrared Raman spectra of peony seed oil and corn oil. According to the attribution of characteristic peaks, the difference in Raman spectra peaks could well reflect the difference in fatty acid content between vegetable oils. A model combining long short-term memory network and convolutional neural networks (LSTM-CNN) was proposed for quantitative prediction of corn oil adulterated with peony seed oil, and its prediction performance was compared with the partial least squares regression algorithm. The results show that the portable 785 nm Raman spectrometer had good stability and could achieve online and rapid quantification of peony seed oil adulteration levels. Combined with the LSTM-CNN model, it could predict the content of corn oil adulterated peony seed oil. The model evaluation parameter's coefficient of determination (R2) was 0.990 8, and the root mean square error (RMSE) was 0.029 9. Portable near-infrared Raman spectroscopy combined with the LSTM-CNN model is a fast, efficient, and feasible method to identify the adulteration of corn oil with peony seed oil.