Aiming at the characteristics of high dimension and complexity of gastric mucosal tumor cell images, a new method based on Fisher Discrimination Dictionary Learning and Regularized Robust Coding (FDDL-RRC) was proposed for the recognition of tumor cell images, so as to improve the robustness of sparse representation for image recognition. Firstly, all the original stained tumor cell images were transformed into gray images, and then the Fisher discrimination dictionary learning method was used to learn the global features of training samples and obtain the structured dictionary with class labels; lastly, the new discriminative dictionary was used to classify the test samples by the model of RRC. The model of RRC was based on Maximum A Posterior (MAP) estimation, and the sparse fidelity was expressed by the MAP function of residuals, so the problem of identification was converted to the optimal regularized weighted norm approximation problem. The highest recognition accuracy rate of the proposed method for tumor cell images can reach 92.4%, which indicates that the presented method can effectively and quickly distinguish the tumor cell images.