The radioactive source density detection system is an instrument that uses radioactive isotopes to measure material density. It has applications in petrochemical, mining, and medical industries. The principle is that the radiation source signal utilizes its excellent penetrability to pass through a sealed pipeline containing substances, be detected by scintillators and photomultiplier tubes, and converted into voltage pulse signals. However, the complex environment of chemical sites can affect the performance of the system, with the most obvious and intuitive being the impact of low temperatures. Compared to the performance of the system at calibration temperature, the output pulse count rate of the system significantly decreases in low-temperature environments, reaching up to 30% at most, resulting in a decrease in measurement density accuracy and even causing misoperation by on-site personnel, making the system output unreliable. This article introduces a temperature compensation method based on probabilistic principal component regression model, analyzes the changes of key detection devices such as scintillators and photomultiplier tubes in the system at low temperatures, collects attenuation data, and constructs a PPCR model without hardware modifications. The maximum expected estimation algorithm is used to estimate the attenuation parameter set of the model and compensate for it. The test results show that this compensation method can control the count rate loss below 3% in low-temperature environments, improving the density detection accuracy of the system in low-temperature environments.