Pecan is one of the top ten nuts in the world. Because of its good taste and rich nutrition, it is loved by people. But pecan is easy to deteriorate in the process of production and processing. Mistaken food can cause many hazards to human body. To solve this problem, a method for detecting the quality of pecans was proposed based on multifeature fusion and level set. Taking thinshelled pecans as research object, and the original image was preprocessed to solve the problem that the target object did not match the background area. The adaptive DRLSE method with improved edge indication function was used to segment the pecans in the image, and the statistical features of the gray histogram of the image were extracted. Multifeatures such as cooccurrence matrix, Tamura and local binary mode were combined and analyzed. The SVM discriminant model was established to realize the nondestructive quality detection of pecans. The experiment collected 200 normal, rancid pecans sample images, and subjected to image rancidity and multifeature analysis. The experimental results showed that the adaptive DRLSE segmentation method with improved edge indication function can complete the segmentation better than the traditional method even inside or outside the target. The accuracy of the method was as high as 96.15% in judging whether pecan was rancid or not, and on this basis, the average recognition rate was 90.81% in judging the degree of pecan rancidity.