Gender identification of embryonated eggs in China has always been a key issue in poultry industry development. In poultry meat production, males tend to be bred, while the egg production industry tends to breed females. If the male and female eggs can be identified in the early hatching process, it will reduce the cost of poultry hatching industry improve the economic benefits of poultry egg and meat production industry. This paper takes duck eggs as the research object. To realize the gender identification of duck eggs at the early hatching stage, a visible/near-infrared transmission spectrum acquisition system was constructed, which can collect the Spectral data of 345 duck eggs hatching from 0 to 8 days with the wavelength range of 200~1 100 nm. A 6-layer Convolutional Neural Network (CNN) for duck eggs’ spectral information was established, including input layer, 3 convolutional layers, 1 fully connection layer and output classification layer. The convolutional layer is used for extraction for the effective information in the spectrum. The full connected layer can integrate the local features extracted by the convolution layer for the classification decision of the output layer. In addition, the introduction of local response normalization and dropout operations in the convolutional neural network can accelerate the convergence speed of the neural network. The convolutional neural network was used to construct a duck embryo male and female information recognition network. By comparing and analyzing the recognition effects of different incubation days, it was found that the recognition effect was the best after 7 days of incubation. Subsequently, the duck eggs’ original spectral data hatched for 7 days were removed for noise, and the 500~900 nm band was selected for subsequent characteristic wavelength selection and modeling. Competitive adaptive reweighting algorithm (CARS), successive projections algorithm (SPA) and genetic algorithm (GA) were used to select the characteristic wavelengths that can distinguish the sex of duck embryos, and the selected characteristic wavelengths are converted into a two-dimensional spectral information matrix. The two-dimensional spectral information matrix retains the effective information of the one-dimensional spectrum and greatly facilitates the combination with the convolutional neural network. They were using a two-dimensional spectral information matrix combined with a convolutional neural network to achieve male and female identification of early hatching duck embryos. After testing, the model based on the SPA algorithm and the CNN network has a better effect, among the accuracy of the training set, development set, and test set are 93.36%, 93.12%, and 93.83%, respectively; the model based on the GA algorithm and CNN network was followed. In other words, the accuracy of the training set, development set and test set are 90.87%, 93.12%, and 86.42%, respectively; the accuracy of the training set, development set and a test set of the model based on the CARS algorithm and CNN network is 84.65%, 83.75%, 77.78%. The research results show that the visible/near-infrared spectroscopy technology and convolutional neural network can realize non-destructive identification of male and female duck embryos in early hatching, which provides technical support for developing subsequent related automated detection devices.