Identification and sorting technology of egg embryo in biological vaccines production with the method of multi-information fusion was researched. The regularity cognition was obtained by the researches on image features, degradation of temperature and variations of transmittance of different types of egg embryo, which indicated that there were many thick radial blood vessels in living egg embryo image and few thin fractured blood vessels in weak egg embryo image, no blood vessels in dead egg embryo image and obvious black blocks features in polluted egg embryo image were found. The rates of temperature decay of living embryo, weak embryo, polluted embryo and dead embryo increased successively, and the transmittance of living embryo decreased gradually with hatching time increase while the variation of the transmittance of other egg embryo were comparatively lesser. The BP neural network model was established to do the egg embryo condition identification based on the information fusion of images, temperature and transmittance. Finally, 80 eggs were randomly selected to collect the information of images, temperature and transmittance to do the verifying experiment, the eggs were hatched six days and taken out from the 37.8℃ incubator and placed under normal room temperature for ten minutes. The result indicated that the identification accuracy of the system was 96.25%, which was increased by 6.25%, 13.75% and 8.75%, respectively, compared with that only using image, temperature or transparency sensors. 