Objective In this paper，combining gray level co-occurrence matrix (GLCM) with wavelet transform of texture arithmetic method extracted X-ray image texture of esophageal cancer with high morbidity in Xinjiang. This method provided auxiliary information with reference value for radiologist and enhanced accuracy rate and efficiency of esophageal cancer. Methods Two types of middle and terminal esophageal cancer: fungating type esophageal cancer and constrictive esophageal cancer were selected in the experiment，and 100 images of the two types of esophageal cancers and normal esophagus were selected，respectively. We used GLCM texture feature method to extract angular second moment，entropy，contrast，correlation and inverse difference moment of variance respectively for X-ray images of esophageal cancer. Meanwhile，we employed wavelet transform to process two-dimensional discrete wavelet decomposition at second level，obtained its high-frequency content images，and extracted the energy features of high-frequency content images as texture features. The C4.5 decision tree was employed as a classifier. Results Eleven features were extracted by GLCM and wavelet transform methods. The experimental results showed that using single feature classification，the accuracy rate of GLCM classification and wavelet transform classification reached to 64.66% and 77%，respectively. The accuracy rate of the comprehensive of GLCM and wavelet transform method was 81.67%，more suitable for the classification of normal esophagus and advanced esophageal cancer. Conclusions This method achieved high classification performance and lay the foundation of the computer-aided diagnosis system of Kazakh esophageal cancer in Xinjiang.