With the continuous development of peoples' life, the skin-care is attracting more attention. There are so many factors affecting the skin condition, and there is nonlinear correlation between factors. However, the current research always use single index to evaluate skin condition which would not be accurate. This article is to introduce seven key factors which affect the skin conditions into the evaluation. The skin condition classification model is built by the BP neural network according to the skin condition index. Firstly, on the basis of age, the skin condition is classified into three type: youth, middle aged, and the old, then the feature of each kind is extracted, and then according to the seven key factors, the neural network calculation is used to study the skin condition, finally, the neural network classification result is compared with the real age to complete the evaluation. The result proves that, by using the method this article introduced in the classification of skin condition, the similarity with the real age can be 70%, BP neural network can evaluate the skin condition effectively. In addition, the method is simple and practical which supplies effective way to do the evaluation. The physical significance of the evaluation is clear and it uses the difference-information in maximum which is the accurate and effective way to obtain the skin condition information.