Existing stemflow sensors based on the thermal equilibrium method are not accurate in measurement, and the stemflow response is not sensitive to transient changes when transpiration is not significant or when the external temperature is low. Therefore, an adaptive stemflow detection system of heat source power was proposed. Taking camphor stalks as the object, a nested experiment based on the thermal equilibrium method of stemflow calibration was designed by comprehensively considering the trend of the proportional change of stemflow in heat source energy, and the sample set of stemflow rates with multi-gradient under different environmental factors such as external temperature, stemflow rate and cross-sectional area were collected. A combined prediction model of heat source power based on support vector regression (SVR) and genetic algorithm (GA) was established. The results showed that the GA-SVR had good accuracy and robustness, its root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R2) were 0.015W, 0.012W and 0.989, respectively. The accuracy verification test suggested that the average relative error of the system was 2.64 percentage points (6℃), 2.53 percentage points (11℃) and 3.68 percentage points (16℃) smaller than that of the FLOW-32KS sensor in the low-temperature section. The adaptive model had a small effect on the correction of the results in the high-temperature section which was similar to FLOW-32KS. It was demonstrated that the stemflow detection system improved the accuracy of the heat balance stemflow measurement after embedding the GA-SVR heat source power adaptive model.