Deep learning has achieved remarkable results in the field of facial recognition. However, when dealing with facial images under complex conditions such as occlusion, illumination and improper angles, predicting a large number of facial feature points is still a challenging problem. In order to solve the localization problem of multiple facial feature points under complex conditions, this paper designs a network structure based on C-Canny algorithm and improved single neural network. The traditional Canny algorithm is applied to the face region localization stage, so that the neural network can quickly reposition the face region to improve the accuracy of model recognition. Experimental results show that, compared with some existing traditional algorithms and neural networks, the neural network structure reduces the value of loss function by 12.2% on average on the 300-w and 300-vw datasets.