Objective To establish a random forest model based on target region segmentation and feature extraction of the dataset so as to classify the risk of early lung cancer. Methods The BiSeNet algorithm was used to realize image segmentation, and the segmented image was converted to YCbCr color space model. The non-skin-color points were found through the values of CB and CR components, and the non-skincolor points were filtered by 9×9 mean filter. The color feature value was extracted under the color model, then the image was converted to gray space. The texture feature value was obtained on the gray level co-occurrence matrix. These eigenvalues were used as inputs to construct a random forest classification model, and ID3 algorithm was used to construct a decision tree. The optimal classification model was found by adjusting the number of decision tree and the maximum eigenvalues. Results The segmentation accuracy of BiSeNet facial image was 96.25%. In YCbCr color space, it had the feature of elliptical skin color clustering, and non-skin-color points could be detected. After adjusting the two parameters (the number of decision tree and the maximum eigenvalues), it is found that when the values of the two parameters were 30 and 4 respectively, the random forest model had the best performance, and the accuracy rate could reach 87.34%. Conclusion The early lung cancer can be classified according to the facial color features and texture features. The experimental analysis shows that the facial red features and texture features of the patients with lung cancer are significantly different from those of patients without lung cancer, which contribute to realize the classification and judgment of early lung cancer, and provide auxiliary evidence for the clinical discovery of early lung cancer.