Gastrodia elata is an essential traditional Chinese medicine. Its powder is often used in traditional Chinese medicine preparations, health products, food additives, and other fields. However, illegal traders will add other ingredients to the powder to reduce costs or increase weight. To achieve a rapid, non-destructive, and susceptible laser spectral detection technology for detecting the adulteration behavior of Gastrodia elata powder, this paper conducted an experimental study on the Raman fluorescence spectrum of Gastrodia elata powder adulteration. Through the spectral research of pure natural Gastrodia elata powder and starch, the characteristics of the starch Raman spectrum were analyzed, and a quantitative model based on multidimensional scale transformation combined with a support vector machine (MDS-SVM) was proposed. By measuring the Raman fluorescence spectrum of simulated adulterated powder, the mds-svm quantitative model for Gastrodia elata powder adulteration prediction was established. The prediction effect of the model was good; the determination coefficient of the prediction results of the model on the test set was 0.8933, and the root mean square error was 0.0131. The results show that Raman fluorescence spectroscopy has the performance of characterizing the composition information of Gastrodia elata powder and combining it with the MDS-SVM algorithm, which can quickly identify the adulteration behavior of Gastrodia elata powder. This paper provides a rapid and non-invasive method for the rapid identification of Gastrodia elata powder and a reference for the law enforcement application of relevant departments.