Objective To construct a gastroscopic image recognition model based on transfer learning and to explore its diagnostic value for gastric cancer. Methods The clear white-light gastroscopic images from 2 001 gastric cancer patients, 2 119 gastric ulcer patients and 2 168 chronic gastritis patients were collected. All these images were divided into training set image group (1 851 gastric cancer, 1 969 gastric ulcer, and 2 018 chronic gastritis) and testing set image group (150 gastric cancer, 150 gastric ulcer, and 150 chronic gastritis). Champion models VGG19, ResNet50 and Inception-V3 in ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition were used as pre-trained models. These models were revised for model training. The training set images were assigned to train the above 3 models, and the testing set images were assigned to validate the models. The whole training process was divided into 2 steps (pre-training and finetuning). Results It was found that ResNet50 ranked No.1 in terms of testing accuracy. Its diagnostic accuracy for gastric cancer, gastric ulcer and chronic gastritis reached 93%, 92% and 88%, respectively. Conclusion Based on transfer learning, the gastroscopic image recognition software model constructed by ResNet50 model can more accurately differentiate gastric cancer from benign gastric diseases (gastric ulcer and chronic gastritis).