The long-wave infrared (LWIR) quantum-well photodetector (QWIP) operates at low temperatures, but is prone to focal plane temperature changes when imaging in complex thermal environments. This causes dark current changes and generates low-frequency temporal dark current noise. To address this, a dark current noise correction method based on dark pixels is proposed. First, dark pixels were constructed in a QWIP system and the response components of imaging pixels and dark pixels were analyzed. Next, the feature data of dark pixels and imaging pixels were collected and preprocessed, after which a recurrent neural network (RNN) was used to fit the dark current response model. Target data were collected and input into the dark current response model to obtain dark level correction values and correct the original data. Finally, after calculation and correction, temporal noise was reduced by 49.02% on average. The proposed method uses the characteristics of dark pixels to reduce dark current temporal noise, which is difficult using conventional radiation calibrations; this is helpful in promoting the application of QWIPs in LWIR remote sensing.