In the downhole oil and gas industry, temperature prediction is an important means to avoid the hazards brought by the high-temperature environment to electronic devices. An improved adaptive Kalman filter (IAKF) temperature prediction method, used here as a virtual sensor, can predict the instrument temperature in real time. It uses the temperature state transfer matrix as a system adaptive discriminant parameter to improve the prediction accuracy of the model. This approach is a data-driven prediction method for practical, field-deployable application, it does not require modeling of heat transfer mechanisms and does not require data sets. Experiments results show that the IAKF model can effectively predict the changing trend of the apparatus in the next 30 min, and the maximum temperature prediction error is within 6.5°C. Its predictions are more stable and accurate than the extended Kalman filter, and it consumes very little CPU resources to run in embedded devices.