Ventilator waveform images can reflect the breathing pattern during mechanical ventilation. Existing respiratory circuit monitoring and management techniques have limitations such as difficulty in real-time monitoring and over-reliance on healthcare personnel experience. In order to realize real-time respiratory pattern monitoring during mechanical ventilation and improve the recognition accuracy of abnormal respiratory patterns, by introducing real-time image acquisition equipment and deep learning-based image recognition technology, real images acquired in the clinic are processed through region of interest extraction based on the NanoDet-Plus model, image preprocessing, and LAMP (Layer-adaptive Sparsity For The Magnitude-Based Pruning) strategy and MagnitudePruner pruning strategy pruning, CWD (Channel-wise Distillation) and BCKD (Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection). for Distillation in Dense Object Detection) distillation of the YOLOv8n-Prune model for waveform image recognition, the algorithm achieves the detection of a variety of abnormal respiratory patterns in the common abnormalities in clinical environments, including water retention, air leakage, secretion accumulation, and patient-ventilator asynchrony, etc., and the model, in the case of the scale getting compressed, mAP50 reaches 0.990 and mAP50:95 reaches 0.879. The algorithm is shown to be interpretable through class activation mapping experiments. Combined with real-time image acquisition equipment, the algorithm can achieve real-time monitoring of abnormal breathing patterns, which has good application value in clinical practice.