Beef meatball is a deep-processed meat product with a unique taste. In the market, some unscrupulous traders cashed in on mixing beef with cheap meat such as pork and chicken to make meatballs. The traditional methods of meat adulteration detection are time-consuming and costly. Hyperspectral imaging technique has the advantages of fast, non-destructive and low cost on meat test. Therefore, the detection of beef meatballs adulterated with pork and chicken was carried out by hyperspectral imaging technique in this study. Adulterated meat was added to the beef meatballs at a level of 0, 5%, 10%, 15%, 20% and 25% of the quality of raw meat respectively. All meatballs hyperspectral data were collected while their spectral data were extracted. The spectral data were pretreated by six methods, first derivative (1st Der), second derivative (2nd Der), mean centering (MC), multiplicative scatter correction (MSC), Savitzky-Golay (SG), standard normal variate transformation (SNVT), which established the Partial least squares model of adulteration content at the full-wave band and obtained the optimum pretreatment method by comparison. After the optimum pre-processing method, the characteristic wavelengths were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), synergy interval partial least squares (siPLS), synergy interval partial least squares-competitive adaptive reweighted sampling (siPLS-CARS), for the purpose of comparing, the prediction effects of models were evaluated on different screening wavelengths methods. The results suggested that the best pre-processing methods of PLS prediction model for beef meatballs adulterated with pork and chicken were MSC and 1st Der. 13, 51 and 32 characteristic wavelengths of adulterated pork spectra were screened by SPA, CARS and siPLS-CARS, respectively. The characteristic subinterval combinations were screened by siPLS: the full-wave band was divided into 14 subintervals, which was then combined with the 1st, 3rd, 7th, and 13th subintervals to establish PLS prediction models. The prediction model of adulterated pork content by CARS wavelength screening method had the best effect, with the RC and RP at 0.981 4 and 0.972 1 respectively, while RMSECV and RMSEP at 0.016 3 and 0.020 3 respectively. 15, 61 and 28 characteristic wavelengths of adulterated chicken spectra were screened by SPA, CARS and siPLS-CARS, respectively. The full spectrum was divided into 15 subintervals by siPLS, combined with the 7th, 8th, 11th, and 12th subintervals to establish PLS prediction models. Analogously, the prediction model of adulterated chicken content by CARS wavelength screening method had the best effect as well, with RC and RP at 0.990 2 and 0.987 8 respectively, and RMSECV and RMSEP at 0.012 3 and 0.012 6 respectively. In this study, compared with siPLS, siPLS-CARS not only reduced the number of characteristic wavelengths but also improved the accuracy of the model prediction. Compared with CARS, it screened for fewer wavelengths, but with slightly lower accuracy. Compared with adulterated pork, the prediction model of adulterated chicken was better on the whole. The research results suggested that hyperspectral imaging technique can realize the content prediction of adulterated pork and chicken in beef meatballs, which provides a theoretical basis for rapid detection of beef meatball adulteration.