At present

facial beauty prediction is facing the problems

in which data is insufficient

the face image is hard to classify

and the deep feature lacks research.To solve these problems

a solution to facial beauty prediction research based on double activation layer depth convolution feature is proposed.Firstly

we use the method of data augmentation and face alignment to increase the number of samples in training set and improve the data quality of database.Secondly

we propose a double activation layer (DAL) to design a CNN model that is more suitable for facial beauty prediction.Experimental results based on 2000 test set show that the method proposed is superior to the traditional method of facial beauty prediction both in classification and regression.In addition

the proposed method achieves better results and real time performance than the state-of-art CNN model

in which rank-1 recognition rate is 61.1% and the Pearson correlation coefficient is 0.8546.Consequently

the DAL method plays an important role in deep facial prediction learning

which can be widely used in face recognition and image processing.