Change-point problems which originated from the field of quality control have become an important area of research. Although existing methods have been successful in detecting change-points, most of them require the underlying data to follow a specific distribution. Heuristically speaking, those methods only perform well when the data set is hypothesised to follow a normal distribution. In this paper, instead of traditional statistical inference, we propose a new algorithm from a shape perspective, which provides a more robust approach to addressing change-point problems. Our new algorithm will define a novel statistic based on shape context, a rich local shape descriptor, to replace the CUSUM test statistic considered by traditional methods. In addition, some areas which do not have change-points are abandoned through segmentation and screening, reducing computational complexity and increasing available storage. At the same time, we introduce the idea of peak recognition, which increases the robustness and effectiveness of the detection. The experimental results demonstrate that the proposed algorithm significantly outperforms some other methods with regard to accuracy and efficiency, especially when a longer time series is under study. We include analyses of two real-world data sets which demonstrate the practical effectiveness of this algorithm.