Abstract: Water quality is critical for aquaculture, with dissolved oxygen (DO) as a key parameter directly impacting organism survival, growth, and farming efficiency. In high-density intensive systems, frequent use of liquid oxygen for aeration to maintain adequate DO increases costs. PID controllers are widely used for DO control due to their simple structure, high robustness, and fast response. However, fixed parameters struggle with nonlinear systems, often causing performance degradation or instability. Researchers have integrated intelligence into PID for online parameter tuning and combined predictive technology to anticipate system dynamics, enhancing adaptability and reducing delays. This study first established a transfer function model of aeration flow vs. DO concentration using experimental data, obtaining the DO control system model. It then proposed an FSSCINET-QNN-PID controller—combining a fast-slow learning sample convolutional interactive network (FSSCINET) for prediction with a quantum neural network (QNN) for PID parameter tuning-to improve response speed and anti-interference capability. FSSCINET enhances DO prediction by integrating adapters (dynamic parameter adjustment) and memory modules (capturing periodic changes) into SCINET, leveraging convolution and interaction for time-series data. QNN enables online updates of PID parameters to handle nonlinear dynamics. A DO monitoring system in industrial aquaculture validated the model. Results showed FSSCINET outperformed SCINET and FSNET, with MSE (0.037 5 mg/L), MAE (0.155 4 mg/L), and RMSE (0.193 7 mg/L). FSSCINET-QNN-PID reduced adjustment time to 1 642 s with smaller overshoot compared to PID and QNN-PID, stabilizing faster with minimal fluctuations. This study can provide a new idea for automatic regulation of DO in factory farming.