Objective：To investigate the correlation between CT radiomics features and pathological classification of pulmonary nodules，and to evaluate the predictive performance of three radiomics models on pathological classification of pulmonary nodules. Methods：A total of 189 patients with pathologically-proven pulmonary nodule and complete clinical data and CT images were obtained from July 2017 to August 2019 in our hospital，including 71 benign nodules，51 non-invasive nodules and 67 invasive nodules. Three radiomics models were established. Model 1 was established to distinguish benign and malignant nodules（including non-invasive and invasive nodules）；model 2 was established to differentiate non-invasive and invasive nodules；model 3 was established to distinguish benign，non-invasive and invasive nodules. The high throughput features from the region of interests（ROIs）within the radiologist-drawn contour were extracted for classification analysis by use of a radiomics software. The classification model was established by selecting features and classifiers intelligently. The prediction performances were evaluated with ROC analysis and AUC. The sensitivity，specificity，accuracy，positive predictive value（PPV），and negative predictive value（NPV）in classification of three models were calculated. Results：Twenty radiomics features were screened out in model 1. Classification test results of the model 1 showed the AUC of 0.85，accuracy of 79.66%，sensitivity of 70.42%，specificity of 84.59%，PPV of 81.74%，NPV of 67.57%，respectively. Two radiomics features were selected in model 2. Classification test results of the model 2 showed the AUC of 0.89，accuracy of 88.06%，sensitivity of 74.51%，specificity of 82.2%，PPV of 81.94%，NPV of 82.61%，respectively. In model 3，20 radiomics features were selected，and classification test results of the model 3 showed the AUC of 0.84，accuracy of 71.34%，sensitivity of 85.05%，specificity of 70.37%，PPV of 83.2%，NPV of 76.3%，respectively. Conclusion：CT radiomics has high value in the identification of benign，non-invasive and invasive nodules.