The rapid and accurate detection of peroxide value is of great significance to the quality of edible oils and food safety control. Near-infrared (NIR) spectroscopy is an ideal method for measuring peroxide values, but the establishment of calibration models requires a lot of resources. In this paper, based on the relationship between near-infrared spectroscopy and lipid peroxide value, the feasibility of establishing a general calibration model for measuring peroxide value in different types and levels of vegetable oils was studied. First, different grades of soybean oil and rapeseed oil were studied. The near-infrared spectra of the two vegetable oils were analyzed by two-dimensional correlation spectroscopy, and the optimal detection band of the general model for peroxide value was selected by interval least squares method (iPLS). Then, the effects of orthogonal signal correction (OSC), standard normal variable transformation (SNV) and second derivative (SD) on the prediction precision of general models were investigated. Further, the prediction performances of three modeling methods, including principal component regression (PCR), partial least squares (PLS) and support vector machine regression (SVR), were compared in detail. At last, four general prediction models for soybean oil (including first-grade and third-grade), rapeseed oil (including first-grade, third-grade and fourth-grade), first-grade vegetable oils (including soybean oil and rapeseed oil) and third-grade vegetable oil (including soybean oil and rapeseed oil) were constructed. The experimental results showed that the change of peroxide value of vegetable oil could be detected by NIR spectroscopy technology, and its spectral changes mainly resided in the region from 1 700 to 2 200 nm. The optimal band, preprocessing method and modeling method of the general model were 1 700~2 200 nm, SD method and PLS method, respectively. Among the four general models, the one for the first-grade vegetable oils (including soybean oil and rapeseed oil) can get better performance. The root means square error of prediction (RMSEP) and the square correlation coefficient (R2) are 0.412 and 0.920, respectively. Compared with the models of first-grade soybean oil or rapeseed oil, the prediction results are roughly the same. It meant that it was feasible to establish a general model with high accuracy between vegetable oils with similar production processes for reducing the workload of repetitive modeling. In addition, in order to expand the versatility of the general model, it was necessary to continuously update the model with new kinds of vegetable oils.