Aiming at the influence of excessive redundant and unrelated attributes on the diagnosis of lung tumors and the fact that Pawlak rough set is only suitable for dealing with discrete variables and causing a large loss of original information, a high-dimensionality of lung tumors with mixed information gain and neighborhood rough set is proposed.The algorithm first extracts the 104-dimensional feature structure decision information table of 3 000 CT images of lung tumors. With the information gain result, the high correlation feature subset is selected, and the high redundancy attribute is eliminated by the neighborhood rough set. The optimal feature subset is obtained through two attribute reductions. Finally, the support vector machine optimized by the grid optimization algorithm is used to construct the classification recognition model to identify the benign and malignant lung tumors.The feasibility and effectiveness of the method are verified from the two aspects of reduction and classification, and compared with the non-reduction algorithm, Pawlak rough set, information gain and neighborhood rough set reduction algorithm.The results show that the accuracy of the hybrid algorithm is better than other comparison algorithms, the accuracy is 96.17%, and the time complexity is effectively reduced. It has certain reference value for computer-aided diagnosis of lung tumors.