The complexity of the meat processing process presents significant challenges in detecting adulteration in meat products. This study uses hyperspectral technology to identify and analyze adulteration in beef meatballs. To establish the models, different proportions (20%, 40%, and 80%) of pork/chicken were added to mince beef to obtain single adulterated samples, respectively. Subsequently, pork and chicken were mixed in 2∶8, 5∶5, and 8∶2 ratios to prepare samples for composite adulteration under three different gradients (20%, 40%, and 80%). In addition, fried adulterated beef balls were also prepared to test the applicability of classification models. Hyperspectral data of the adulterated samples were collected and preprocessed using five different methods. Adulteration identification models were developed using the Extreme Learning Machine Classification (ELMC) and Support Vector Classification (SVC) algorithms. Feature wavelengths were extracted using the Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS), developing corresponding simplified models. The results showed that the performance of the raw/cooked beef ball classification models SVC model based on full wavelength was better than that of ELMC. In contrast, the simplified model based on characteristic wavelength showed a contrary trend. For the classification of raw beef balls, the ELMC model (SPA-ELMC-Raw) established based on the 44 characteristic wavelengths selected by SPA yielded the best performance, with classification accuracies of 97.17% for both the calibration set and prediction set. For the classification of cooked beef balls, the ELMC model (CARS-ELMC-Cooked) established based on the 38 characteristic wavelengths selected by CARS showed the highest performance, with classification accuracies of 97.17% and 96.23% for the calibration set and prediction set, respectively. The results indicated that hyperspectral imaging technology proves to be an effective, rapid, and accurate method for discriminating between different types of adulteration in raw and cooked meat. This provides a strong theoretical basis for developing portable detection equipment.