Cigarette laser code recognition is an important tool for tobacco inspection. This study proposes a method for recognizing cigarette codes based on a dual-state asymmetric network. Insufficient training on samples of distorted cigarette codes leads to the weak generalization ability of the model. To address this issue, a nonlinear local augmentation (NLA) method is designed, which generates effective training samples with distortion to enhance the generalization ability of the model through spatial transformation using controllable datums at the edges of cigarette codes. To address the problem of low recognition accuracy due to the similarity between cigarette codes and their background patterns, a dual-state asymmetric network (DSANet) is proposed, which divides the convolutional layers of the CRNN into training and deployment modes. The training mode enhances the key feature extraction capability of the model by introducing asymmetric convolution for optimizing feature weight distribution. For real-time performance, the deployment mode designs BN fusion and branch fusion methods. By calculating fusion weights and initializing convolutional kernels, convolutional layers are equivalently converted back to their original structures, which reduces user-side inference time. Finally, a self-attention mechanism is introduced into the loop layer to enhance the extraction capability of the model for cigarette code features by dynamically adjusting the weights of sequence features. Comparative experiments show that this method has higher recognition accuracy and speed, with the recognition accuracy reaching 87.34%.