In the process of mass production in solar panels, different patterns which are dark or light will be formed on the surface of the panels due to the production technology. An automatic classification system is needed by the factory to improve work efficiency, so an artificial intelligence identification system as a classifier is proposed to meet this demand. The solar panels will be classified by the system using the surface pattern of solar panels as a reference. Firstly, local binary pattern (LBP) operator is used as classification features, the samples of solar panels will be divided into two categories which are separately named With crystal and No crystal. And then, the local contrast is taken as the classification feature, and the with crystal sample are also divided into two categories such as dark crystal and light crystal. In order to meet the rapid and accurate demands of industrial production, back propagation (BP) neural networks are adopted in auto-recognition as a classifier. Experimental results show that the classification system has fast speed and high accuracy rate, which can meet the demands of actual industrial production line.