In order to improve the aceuraey and efficieney to identify the aspha1t pavement surface distress by the image information a method of asphalt pavement surface image feature representation 15 put forward，whiceh may reduce calculation of pavement surface distress image classification. A pavement surface image 15 divided into 64X64 pixels sub-images and the intensity variances are used to represent the sub-images feature.Meanwhile，the sub-image pattern classifier 15 designed based on BP artificial neural network，all of the sub-images pattern classifying results are arra”damatrix and the pavement surface distress image segmentation 15 represented by this matrix By the experiment to identify the typical surface distress of asphalt pavement，the conelusions are as follows:(a)the average value and a minimum value of intensity variances of the image segmentation are input to BP artificial neural network，50 that the models of distress sub-images can be identified accurately.(b)the counts of horizontal and vertical projection of the distress sub-images models，sum of the distress sub-images，and convinced coefficient of the distress sub-images models may be used to deseript the main features of different the pavement surface distress，(e)the distributing features of the pavement surface distress image segmentation are used to represent a integration feature of the pavement distress image，50 that a good result for identifying Pavement surface distress may be obtained.