A novel algorithm based on multi-dimensional fusion is proposed for classifying lung nodules.Based on the algorithm for reducing false positives of pulmonary nodules,the optimization is carried out by introducing a high-level feature enhancement soft activation mapping module after obtaining features by the multi-scale feature fusion module to improve the classification ability.To address the imbalance of different nodule data in the actual classification,a balanced mean square error loss is adopted to improve the training effect of the model.A three-dimensional and two-dimensional model fusion method is used to further improve the classification performance.The experiment conducted on a Private Lung dataset proves that the proposed model has a classification accuracy of 93.8％,outperforming the existing methods.