Abstract: The hatched egg embryo is the carrier for the production of avian influenza vaccine. The activity detection of the egg embryo is a key link in the production of vaccine. The detection of the egg embryo activity by the photoelectric volume pulse method is the key to improve the accuracy of the egg embryo activity detection rate. In order to improve the detection efficiency and accuracy of egg embryo activity, the sliding power spectrum method was adopted to visualize egg embryo pulse wave, which accurately classified egg embryo activity based on convolutional neural network. The experimental results show that the calculation time of a single egg embryo signal using a convolutional neural network is only 12.6 ms, and the detection efficiency is increased by nearly 200 times in comparison with the manual detection method. The convolutional neural network classification accuracy of the visualized egg embryo pulse wave can reach 94.14%, among which the true positives of live embryos, dead embryos and weak embryos are 99.74%, 93.73%, and 84.39%, respectively. The egg embryo activity classification model based on the convolutional neural network can accurately identify the egg embryo activity in large-scale production, which has important application value for the vaccine production process.