To address issues such as the high number of parameters, significant variations among images of similar dishes, weak geometric invariance, and low recognition rates in Sichuan cuisine recognition methods, a lightweight Sichuan cuisine recognition model, RGBNet, based on residual neural network, is proposed. The model employs dilated convolutions to increase the receptive field of convolutional kernels while maintaining a consistent parameter count, thus obtaining more shallow-level features. An RGB module is constructed using asymmetric convolutions to enhance the model's geometric invariance, feature non-linear expression, and feature extraction capabilities. Finally, the DFC long-range attention mechanism is introduced to effectively capture long-range information, thereby improving adaptive learning capabilities. To validate the model's performance, the classic ChineseFoodNet benchmark dataset is utilized. A MiniChineseFood dataset is created by extracting 30 classes totaling 20,000 images for experimentation. The recognition accuracy is measured using the top1 method of image recognition performance, achieving a final image recognition accuracy of 96.62%. Compared to models such as EfficientNet, ShuffNet, FasterNet, and MobileNetV2, RGBNet demonstrates respective accuracy improvements of 16.57%, 18.52%, 17.12%, and 16.35%. This presents a novel approach for industrial food recognition.