n view of the flaws of high complexity and slow data processing of face recognition found in traditional convolution neural networks, a lightweight convolutional neural network algorithm has thus been proposed. Firstly, the sample data is to be enhanced by clipping and rotating the data set. Then, a lightweight convolutional neural network based on MobileNet is used to extract the features from sample data, and an SSD target detector is used for face recognition in sample data. Finally, the above algorithm is implemented by Python programming, followed by a comparison with the outcome of the traditional face recognition algorithm. The experimental results show that the proposed lightweight convolution neural network algorithm is faster in the processing speed with a lower model complexity and retained accuracy.