The paper quantifies data by using binary coding, builds the syndrome type classification model of colorectal cancer based on BP neural network, analyzes its advantages, and explores the impact of the sample size of 8 syndrome types on Back Propagation (BP) neural network accuracy. By ruling out colorectal cancer syndrome types of a small number of samples, it gains the quantified relation between sample size and accuracy.