Abstract: To better judge combustion cone-falling propensity of cigarette, the detection and classification-recognition models of combustion cone-falling were established. The testing of combustion cone-falling propensity was conducted with 100 kinds of commercial cigarettes, their images of combustion cones were processed via defect rejection, classification annotation and feature enhancing/weakening. With the processed images, a dataset of cone-falling detection and a dataset of cone-falling classification-recognition were set up separately. Three deep neural network models were established by using ResNet, MobileNet and ViT (Vision Transformer) as backbone networks and were trained with the images of combustion cone-falling. The performance of the models was evaluated with precision (P), recall rate (R), f1 score and model capacity. The results showed that: 1) The three models performed well on cone-falling detection (model lose value L < 0.05, P > 99%, R > 99%); however, the ViT model was not suitable for cone-falling classification-recognition due to its loss value in the dataset of classification-recognition failed to converge (P=86.05%, R=76.04%). 2) The MobileNet model selected based on f1 score and model capacity had the advantages of higher accuracy and operational speed, its average precision for cone-falling detection and classification-recognition was 99.64% and 89.50% respectively. This method provides a support for studying the apparent combustion property of cigarette.