Faced with the continuous improvement of counterfeiting methods, cheap olive pomace oil will probably become a potential raw material for adulterating extra virgin olive oil. Therefore, this study focuses on the deep learning algorithm-assisted non-contact, non-destructive spectral detection technology to quantify the adulteration behavior of extra virgin olive oil. Mix expired olive pomace oil and extra virgin olive oil in different volume proportions to prepare different adulterated, mixed oil concentrations. The 785 nm portable Raman spectrometer was used to collect the Raman spectra of these mixed oils, and the quantitative analysis model of adulteration was established by combining the one-dimensional convolutional neural network algorithm. The density functional theory B3LYP/6-31+G (d, p) basis set was used to calculate the Raman spectrum of linoleic acid molecules to further analyze the Raman spectrum of extra virgin olive oil. The experimental results show that the technical solution based on combining deep structured feedforward neural networks and 785 nm portable Raman spectroscopy technology is a powerful tool for quantitative analysis of plant oil adulteration. The decision coefficients of 4000 spectral data quantitative models from 80 mixed oil products are all better than 0.97, and the decision coefficient of quantitative analysis in the evaluation model test set reaches 0.9704, with a root mean square error less than 0.0499. This technology has great application potential in quickly evaluating the adulteration of extra virgin olive oil, providing a beneficial reference scheme for regulating the domestic olive oil market and safeguarding consumers' legitimate rights and interests.