As the pioneering project in China utilizing TBM construction for such shafts, the Luoning pumped storage power station employs a gravity-driven muck discharge process via chute, replacing traditional belt conveyance. While significantly reducing equipment investment and energy consumption, this method poses challenges including inefficient manual monitoring and control of muck flow status, water wastage, and high labor consumption. To address these issues, high-definition cameras are used to continuously image the state of the muck flow in the chute in real time. Based on the observed water-to-muck ratio within these images, a six-category classification system for muck flow status was established: idle status, water-deficient status, semi-water-deficient status, optimal status, semi-water-rich status, and water-rich status. Four distinct convolutional neural network architectures (DenseNet121, ResNet152, Wide_ResNet50_2_L, and EfficientNetV2) are subsequently employed for intelligent muck flow status classification, all utilizing identical hyperparameter optimization. Model comparison demonstrates that EfficientNetV2 achieves superior performance, attaining a weighted F₁ score and A of 0.883 on the test set, with a per-image prediction time of 0.109 s. This approach effectively fulfills the stringent real-time and accuracy requirements for intelligent muck flow status recognition in practical engineering applications, promoting safer and more efficient automated construction.