Olive oil has become the main category with increasing daily consumption of vegetable oils due to its high nutritional characteristics. According to the processing technology, olive oil is divided into different quality grades such as virgin, refining and blending. Because the prices of different grades of olive oil are quite different, the olive oil market has a certain degree of real attribute problems, such as substandard quality. At the same time, there are complex indicators related to grade identification, and most of the corresponding physical and chemical testing methods involve large-scale laboratory equipment with high testing costs, low efficiency and heavy workload. Our country is a major importer of olive oil, and adopting the model of product standard confirmation and determination of indicators one by one, it cannot meet the rapidly increasing requirements for rapid customs clearance of imported products. This research focuses on the rapid quality assessment requirements of imported olive oil at the port supervision site. It develops a method of simultaneous collection of multi-spectral information and dimensionality reduction fusion imaging, which combines the characteristic data of the visible-ultraviolet spectrum and the Raman spectrum to construct the Raman-Ultraviolet, visible 2D spectrum. Then the fingerprint feature is judged by two-dimensional imaging. The standard 2D fusion imaging source map of extra virgin olive oil, refined olive oil and pomace oil is constructed, which is used as the grade discrimination standard to compare the two-dimensional spectrum to determine the olive oil grade visually. Finally, combined with the spatial angle value conversion algorithm, the olive oil grade is qualitatively judged. Through the angle value calculation, the angle between extra virgin olive oil and refined olive oil is between 0.794 7 and 1.094 7, and that of olive-pomace oil is between 1.157 0 and 1.319 8. The angle values between the extra virgin olive oils are all less than 0.1, which can be used to determine the grades of different olive oils, and the angle decision model is used for quantitative analysis of olive oil adulterated samples. Preparing mixed samples of different olive oil grades and calculating the model correlation coefficients of extra virgin mixed refined olive oil and pomace oil to be 0.994 2 and 0.991 0, respectively. Different samples are taken for verification, and the relative error is between -4.48%% and 2.58%. This study uses Raman-UV-Vis fusion spectroscopy combined with chemometrics to establish a standard two-dimensional spectrum to visually determine the grade of olive oil and establish a virgin olive oil adulteration detection model for quantitative analysis of olive oil content to achieve food quality and safety at the port Multi-dimensional, high-precision and high-accuracy visual display of risk information. Adopting the rapid screening method of imported olive oil quality grade can effectively improve the monitoring efficiency of the port’s attention to risks, improve the accuracy of imported food supervision, and provide technical support for the intelligent transformation of the port food risk monitoring mode.