Objective: This study aims to design a novel approach, utilizing computer vision combining with deep learning, for rapid determination the adulteration in star anise powder. Methods: Collected the original images of star anise powder with varying adulteration ratios. Employing preprocessing and data enhancement techniques, an image dataset was curated. Subsequently, a SqueezeNet model was constructed and compared with five machine learning models, including Support Vector Machine （SVM）, K-Nearest Neighbor Learning （KNN）, Random Forest （RF）, Gradient Boosting Tree （GBT）, and Multilayer Perceptron （MLP）. Results: The highest accuracy achieved by the five machine learning models was only 66.37%, while the accuracy of the SqueezeNet model was 99.42%. The results showed that SqueezeNet model was better than these machine learning models in identifying the adulteration in star anise powder. Conclusion: The proposed detection method based on computer vision combining with SqueezeNet model can effectively identify the adulteration in star anise powder. This method is easy to operate, and provides a novel technique for the rapid detection of food adulteration.