In order to solve the problem of incomplete feature extraction in lung computed tomography (CT) image and the gradient disappearance caused by network degradation with the deepening of convolution network, an automatic classification model of pulmonary nodule malignancy based on multi-scale feature fusion network (MSFFNet) is proposed. The multi-scale convolution operation is used to extract and fuse the features of different ranges of input CT images. The SE-ResNeXt module is introduced to make full use of the channel attention mechanism to effectively solve the problem of feature information loss. Finally, the classification results of benign and malignant pulmonary nodules are obtained. The accuracy of MSFFNet classification model achieves 97.2%, and the specificity and sensitivity achieve 96.14% and 98.62%, respectively, which are better than those of SE-ResNeXt.