The advent of deep learning and convolutional neural networks, in conjunction with the unremitting expansion and refinement of face recognition datasets, has precipitated a substantial advancement in face recognition technology based on convolutional neural networks. Nevertheless, in real-world implementation scenarios, numerous disadvantages persist in the deployment of facial recognition technology. The present study focuses on the research of face recognition algorithms based on lightweight convolutional neural networks. A thorough analysis of prevalent facial recognition architectures is conducted, encompassing an examination of numerous network models. The integration of diverse network models strengths is achieved to engineer a lightweight network, designated as Mobi-FaceNeXt, for the purpose of facial feature extraction. While ensuring the accuracy of face recognition, efforts are made to minimize network parameters and computational load. This makes the algorithm deployable on general embedded platforms and devices with limited computing and storage resources. This has significant practical engineering implications. In the research, a joint loss function of MagFace Loss and Center Loss is used for training, and the processed MS-Celeb-1M dataset is utilized to enhance the learning ability of the deep face recognition model for facial features. Depth-wise separable convolution is employed to reduce parameters and computations, and the algorithm is optimized to enhance the network processing speed, thereby facilitating the extraction of facial feature information. The experimental results demonstrate that the Mobi-FaceNeXt model can achieve a superior level of face recognition accuracy while maintaining a low level of network computations and parameters. The technology in question has the capacity to satisfy the requirements of embedded devices and to extract facial feature information with greater efficiency. This suggests a broad range of potential applications.