Patient-ventilator asynchrony (PVA) commonly occurs during mechanical ventilation. Considering the developing trend of physiological loop ventilation and weak generalization and high complexity of public methods, this paper firstly mixes different ventilation modes simultaneously as sample, and then two cross validations, Hold-out and Leave One Subject Out, are introduced to verify the feasibility of the task that classifying PVAs under hybrid ventilation modes. To solve the drawback of current models, the phase-space reconstruction-based convolutional neural network (PSR-CNN) model is proposed. During model selection, zeropadded and down sampling are applied in order to ensure that all experiments could be conducted smoothly. Results suggest that the performances of PSR-CNN have a higher accuracy and a F1-score than other algorithms. In addition, PSR-CNN shows a shorter time with regard to average training consumption. Overall, this study indicates that the proposed model has a stronger generalization and a decrease in the complexity, which shows application value and provides reference for the intelligent promotion of ventilators in engineering.