The Screen content images (SCIs) are images containing textual and pictorial regions, which have become more and more connected with our daily life with the widespread adoption of multimedia applications. In particular, the image quality assessment (IQA) of SCIs is important because of its good property to guide and optimize lots of image processing systems. However, the no-reference (NR) IQA algorithms receive little attention and achieve unsatisfactory performance. Hence, this paper proposes a novel no-reference IQA method based on patch-wise multi-order derivatives for SCIs. This method includes two stages: patch-wise image quality evaluation and quality pooling. The first stage focuses on learning visual quality of local regions. Two features of image patches are extracted: multi-order derivative statistics, multi-order derivative histograms, which respectively describe the global and local information of the multiorder derivatives. Then the support vector regression (SVR) is applied to measure visual quality of image patches given a set of extracted features. The second stage aims at pooling patch-wise quality to an overall quality score with weights derived from entropy of gradient information of SCIs. Experimental results show that our method obtains superior performance against state-of-the-art NR-IQA approaches on the SIQAD database of SCIs, and also achieves competitive performance against state-of-the-art FR-IQA methods for SCIs.