A non-destructive detection method to identify damage caused by Conopomorpha sinensis in litchi fruit was examined with the help of multiple spectral techniques. The visible/near-infrared spectra, hyperspectral images, and X-ray imaging data of litchi were collected. Multiplicative scatter correction and standard normal variate transform were adopted to preprocess the spectra. Subsequently, characteristic wavelengths were obtained from the spectra using the successive projections algorithm (SPA). After that, partial least squares regression (PLSR) and support vector regression (SVR) were performed on the three spectral methods individually and the proposed multi-spectral fusion method. The results suggest that when one single method is used for detection, the MSC+SPA+SVR model established based on the visible/near-infrared spectra of litchi gives the best results. The performance indicators of the training set are R2=0.84 and RMSE=0.20, while those of the test set are R2=0.79 and RMSE=0.23. SVR modeling results of different combinations of spectral techniques demonstrate that combining visible/near-infrared spectra with X-ray imaging data provides optimal detection results. The performance indicators of the training set are R2=0.90 and RMSE=0.15, and those of the test set equal R2=0.84 and RMSE=0.19, with a detection accuracy of 95.00%. Therefore, multi-spectral fusion of visible/near-infrared spectra and X-ray imaging data enables better detection results for damage caused by Conopomorpha sinensis in litchi fruit. These findings give insights to future research on the development of related equipment.