With the development of machine learning theory and image processing technology, peoples are more and more interested in how to build a system to automatically evaluate and assess aesthetics quality of photos in the field of computer vision and computational aesthetics. This type of system can be a supplement for the subjective assessment of photo aesthetic quality. In this paper, 25 visual features extracted from each image are used to objectively evaluate photo aesthetic quality, which can better reflect the aesthetic quality of portrait photo. Four aesthetics classifiers are built based on support vector machine, AdaBoost, random forest and linear regression. 10-Fold cross validation experiment is performed to reveal which features have a salient impact on the aesthetic assessment. Compared to the current research results, classifiers using 25 features proposed by this study have higher classification accuracy rate for portrait photo aesthetic evaluation, even using the smaller training sets.