Honeycomb lung is a CT imaging manifestation of various advanced lung diseases, characterized by diverse cystic lesions presenting a honeycomb-like appearance. Existing computer-aided diagnosis methods struggle to effectively address the low identification accuracy caused by the varied morphology and different locations of cellular lung lesions. Therefore, a combined CNN and Transformer model guided by lesion signals is proposed for cellular lung CT image recognition. In this model, a multi-scale information enhancement module is first employed to enrich the spatial and channel information of features obtained by CNN at different scales. Simultaneously, a lesion signal generation module is used to strengthen the expression of lesion features. Subsequently, Transformer is utilized to capture long-range dependency information of features, compensating for the deficiency of CNN in extracting global information. Finally, a multi-head cross-attention mechanism is introduced to fuse feature information and obtain classification results. Experimental results demonstrate that the proposed model achieves accuracies of 99.67% and 97.08% on the honeycomb lung and COVID-CT dataset, respectively. It outperforms other models, providing more precise recognition results and validating the effectiveness and generalization of the model.