The edible oil in the humandaily diet is rich in saturated fatty acids, which can provide energy and other healthy nutrients for the human body, but excessive intake of saturated fatty acids can lead to a variety of cardiovascular diseases. In this study, a method for analyzing the content of saturated fatty acidsin edible oils was developed by combining reflectance spectroscopy and machine learning. Firstly, the reflectance spectra of 7 edible vegetable oils, such as rapeseed oil, soybean oil, sunflower seed oil, corn oil, olive oil, sesame oil and peanut oil, were measured in the range of 350~2 500 nm, as well as the contents of palmitic acid, arachidonic acid and behenic acid were obtained by GC-MS. Spectral preprocessing algorithms were employed to eliminate the noise in spectra, including centralization, multiple scattering correction, standard normal variable transformation and standardization. Then, a novel two-dimensional spectral convolution regression network (S2DCRN) was constructed for fatty acids analysis, and a full convolutional network (FCN), partial least squares regression (PLSR), support vector regression (SVR) and random forest (RF) were compared with S2DCRN. Finally, sequential forward selection (SFS), random frog (RFrog) and genetic algorithm were used to select important wavelength spectra to re-build more simple and robust analysis models. The results showed that the S2DCRN obtained optimal performance after pretreatment of edible oil spectra with the determination coefficient of prediction set (RP2) of 0.987 9 and the root mean square error of prediction set (RMSE_P) of 0.510 0. Based on important wavelengths selected by combination RFrog and SFS, the S2DCRN exhibited excellent performance with RP2=0.967 9 and RMSE_P=0.462 7. Although the results based on important wavelengths obtained is slightly worse, the number of wavelengths is less than 1% of the full spectra. It is convenient for the measurement of spectra and remarkably reduces the complexity of the model, which is helpful for the further development of portable and simplified detection devices. In addition, to further explore the generalization and applicability of S2DCRN, S2DCRN was used to analyze the content of arachidonic acid and behenic acid and gain a prediction result for arachidonic acid RP2=0.950 1, RMSE_P=0.152 9. Therefore, the proposed method accurately and rapidly analysed various fatty acids in edible vegetable oils by reflectance spectroscopy.