Abstract: In order to rationalize and facilitate the application of deep learning technology into the appearance quality inspection of cigarette products, an image algorithm based on Local Characteristic Similarity Metric (LCSM) was proposed, with the LCSM algorithm the only need is training normal samples. A specific convolutional neural network was used to extract the image features from the normal samples and construct the data feature distribution of each region in the image. The local characteristic vector of the test image is extracted, and Wasserstein distance is used to measure the similarity between its feature distribution and that of the corresponding normal sample so as to judge whether there is any defect in the test image. The data sets of cigarette packet, breakable capsule, and cigarette were established for verifying the performance of LCSM algorithm. The results showed that the inspection accuracies of LCSM algorithm for the three data sets reached 98.75%, 99.50%, and 98.50%, which were higher than the recently reported accuracies of Skip-GANomaly, STPM, and IGD algorithms by 14, 2, and 2 percentage points, respectively. This method provides technical support for improving the inspection accuracy of appearance defects of cigarette products.