In recent years, with the rapid development of computer technology, perception of human facial beauty is an important aspect of human intelligence and has attracted more and more attention of researchers. For the current study methods that exist in the training data set of scoring most depends on manual processes, and the facial beauty assessment is not detailed enough to predict the results, this paper aims to investigate and develop intelligent systems for learning the concept of female facial beauty with data mining learning and producing human-like predictors. Our work is notably different from and goes beyond previous works. We impose less restrictions in terms of pose, lighting, background on the face images used for training and testing, which greatly reduces the manual operation for classification and we do not require costly manual annotation of landmark facial features but simply take raw pixels or texture feature as inputs. We show that a biologically-inspired model with clustering and the improved BP network method can produce results that are much more human-like approach.