To enhance the accuracy of photovoltaic power level classification to meet industry requirements， a classification model based on a Res-MobileCom parallel network was proposed. In data preprocessing， denormalized bilinear interpolation was used to maximize the preservation of data features. Subsequently， through parallel training of simplified residual network（ResNet） and efficient convolutional neural networks for mobile vision（MobileNet）， their outputs were jointly fed into the channel estimation-signal detection network（ComNet） for further data feature extraction， ultimately obtaining the classification results. The experimental results demonstrated that compared to common deep learning models， the Res-MobileCom model retained the feature extraction capability and lightweight nature of ResNet and MobileNet， exhibiting good balance and generalization ability. By using the denormalized bilinear interpolation method and the ComNet for further data feature extraction， the model accuracy improved by more than 10 percentage points， providing a novel approach and idea for improving the accuracy of photovoltaic power level classification models. Future work will focus on stability optimization， cross-task validation， and engineering deployment.
