The invention provides a classification method and a classification system for cancer digital pathological cell images. According to the classification method and the classification system, a suspected lesion region of interest is subjected to block processing, the suspected lesion region after block processing is subjected to feature extraction by utilizing partial matching pattern textural features, and the extracted features are classified and identified by adopting an extreme learning machine training method, so as to determine benign and malignant tumors and differentiate levels. The classification method and the classification system for the cancer digital pathological cell images utilize the partial matching pattern textural features for conducting feature extraction, analyze the textural features of cells from macroscopic and microscopic aspects, have the advantage of rotation invariance, effectively overcome the problems of diversity, irregularity and the like of cell morphology, provide reliable textural feature information for classification, apply an extreme learning machine to the classification of breast cancer cells, shorten the training time, accelerate the speed of classification and identification, and improve the accuracy of recognition.