To recognize an individual goat under farm conditions, a novel goat face recognition model named DWT-GoatNet was proposed based on wavelet transform and convolutional neural networks, which integrated frequency domain features and spatial domain features. Firstly, facial images of a total of 30 highly similar Xinong Saanen dairy goats were collected under two different light conditions, daytime and night. Some images were discarded based on structural similarity (SSIM), and the remaining images were cropped manually. Image sets were also augmented by operations of blur, brightness adjustment, translation, rotation, noise addition and scaling. Secondly, a goat face feature extraction module was designed based on twodimensional discrete wavelet transform (2D-DWT) and convolution operation to achieve feature fusion. Then, with this module, a classification module was added and a convolutional neural network named DWT-GoatNet was built. Finally, the combination of hyper-parameters was optimized and goat face recognition model was formed. The experimental results showed that the accuracy of the proposed goat face recognition model can reach 99.74% and 99.89%, respectively, on test set under different light conditions of daytime and night, which was higher than that of some classical CNNs such as AlexNet, VGGNet-16, GoogLeNet, ResNet-50 and DenseNet-121, while the DWT-GoatNet can provide an effective recognition for some related fields of precision farming and agricultural insurances.