The invention discloses a method for pylorus and ileocecal valve positioning through wireless capsule endoscope images. When a wireless capsule endoscope is used for examination, the starting point and the stopping point of the small intestine are accurately found so that the workload of a doctor for viewing the images can be reduced, and missed diagnosis is reduced. According to the method, a deep learning thought serves as a technical core, a transfer learning strategy is also used, and a convolution neural network algorithm in a deep learning model is used for building an area image classifier; features of the wireless capsule endoscope images are obtained through automatic learning by means of training of the model, and then an area image identification result sequence is analyzed through an area positioning algorithm, so that pylorus and ileocecal valve positioning in the wireless capsule endoscope images is achieved. The method makes up for the blank of an existing capsule endoscope in the field of intelligent identification and accurate positioning, the working intensity of a doctor is greatly reduced, the working efficiency and the diagnosis rate are improved, the practicalvalue of the capsule endoscope in the clinical diagnosis of digestive tract diseases is further promoted, and a more efficient and more standard diagnosis mode is formed.