Total volatile basic nitrogen (TVB-N) content is an important index in evaluating the pork′s freshness. We attempt to determine TVB-N content in pork by multi-information fusion technique based on spectroscopy and imaging. In experiment, pork samples with different freshness are studied, and the near-infrared spectra and images are collected simultaneously. Principal component analysis (PCA) is implemented on these feature variables from image and spectral information, and a prediction model is developed by the back-propagation artificial neural network (BP-ANN). Experimental results show that the model based on multi-information fusion is superior to the model based on a single technique, the root mean square error of cross-validation in the model is 1.2975, and the correlation coefficients is 0.957 when the model is tested by independent samples in the prediction set. The overall results show that it is feasible to measure TVB-N content in pork by multi-information fusion based on spectra and imaging, and the performance from the model based on multi-infusion fusion is better than that from the model based on a single technique.