Objective This study aims to explore the application value of spectral CT and perfusion CT parameters in the pathological classification and prognostic assessment of lung cancer. Methods A total of 94 lung cancer patients confirmed by pathology at Shanghai Chest Hospital from January 2023 to November 2024 were included in the study， including 49 cases of lung adenocarcinoma （LUAD）， 30 cases of lung squamous cell carcinoma （LUSC）， and 15 cases of small cell lung cancer （SCLC）. All patients underwent spectral CT combined with perfusion scanning using a 256-slice Revolution Apex from GE. Two radiologists independently measured the spectral and perfusion parameters of the three groups of images， including spectral curve slope （K）， iodine concentration in the lesion area （ICL）， effective atomic number （Zeff）， surface permeability （PS）， and perfusion index （PI）， and established a lung cancer pathological subtype discrimination prediction model based on spectral CT radiomics features. All subjects were randomly divided into a training group and a validation group at a ratio of 3∶1. The discrimination efficacy of the spectral discrimination model between different pathological subtypes and the discrimination efficacy of arterial and venous phase images were compared in multiple dimensions. The performance of the model was evaluated using the receiver operating characteristic （ROC） curve. Results Statistical analysis showed that the spectral curve slope， ICL， NIC， and Zeff of LUAD patients were significantly higher than those of LUSC and SCLC patients （P < 0.05）， while there were no significant differences in these parameters between LUSC and SCLC patients （P > 0.05）. Among the perfusion CT parameters， surface permeability （PS） showed significant differences among the three groups （P < 0.05）， while blood volume （BV）， blood flow （BF）， perfusion index （PI）， time to peak （TTP）， and mean transit time （MTT） did not show statistical differences. The multi-factor logistic regression model based on spectral parameters showed strong discriminatory performance： the area under the curve （AUC） of the LUAD and LUSC discrimination model was 0.806/0.77 （training group/test group） in the arterial phase and 0.867/0.9 （training group/test group） in the venous phase； the AUC of the LUAD and SCLC discrimination model was 0.885/0.883 （training group/test group） in the arterial phase and 0.851/0.776 （training group/test group） in the venous phase. Conclusion This study indicates that the multi-dimensional functional metabolic analysis indicators of spectral and perfusion CT imaging have significant value in the differential diagnosis of lung cancer pathological subtypes. The diagnostic model constructed by combining multiple spectral parameters can significantly improve the discrimination efficacy of lung adenocarcinoma， squamous cell carcinoma， and small cell lung cancer， providing precise imaging evidence for the formulation of individualized treatment plans.