When using AlexNet to implement image classification of gastric tumor cells, there are problems of small dataset, slow model convergence and low recognition rate. Aiming at the above problems, a Data Augmentation (DA) method based on Radial Transformation (RT) and improved AlexNet was proposed. The original dataset was divided into test set and training set. In the test set, cropping was used to increase the data. In the training set, cropping, rotation, flipping and brightness conversion were employed to obtain the enhanced image set, and then some of them were selected for RT processing to achieve the enhanced effect. In addition, the replacement activation of functions and normalization layers was used to speed up the convergence and improve the generalization performance of AlexNet. Experimental results show that the proposed method can implement the recognition of gastric tumor cell images with faster convergence and higher recognition accuracy. On the test set, the highest accuracy is 99.50% and the average accuracy is 96.69%, and the F1 scores of categories:canceration, normal and hyperplasia are 0.980, 0.954 and 0.958 respectively, indicating that the proposed method can implement the recognition of gastric tumor cell images well.