At present，there are intrinsic differences between machine recognition of facial expression and human perception in the facial expression recognition system

which affect the precision of facial expression recognition. In order to reduce the semantic gap between the low-level visual features of face images and high-level semantic

a novel facial expression recognition method based on semantic knowledge of analytical hierarchy process(AHP) is presented. The analytical hierarchy process method is adopted to describe the high-level semantic of face images of the training set

which further used to establish semantic features.In the stage of low-level visual features extraction

the 2nd-order principal component analysis method is proposed to extract the texture features of face images. In the recognition stage

only low-level visual features of the input face image is used

and k-nearest neighbor method combined with semantic features in the study stage is used to classify the facial expressions. The proposed method combines the low-level visual features with high-level semantic features

reducing the semantic gap between them.The experiments are conducted on Japanese Female Facial Expression (JAFFE) database and the overall recognition rate of 9392% is achieved. Theoretical analysis and experimental results both show that the proposed method has higher recognition rate.