Aiming at the neutral point DC monitoring data of the substation obtained during the trial operation of UHV DC transmission project, the data validity evaluation method is proposed. Firstly, according to the neutral distribution DC distribution mechanism of the substation, the influencing factors and the substation bias current distribution are analyzed. Based on the correlation feature, a neutral point DC prediction model based on Artificial Neural Network (ANN) is constructed and compared with the measured data to verify the validity of the prediction model. Then, Monte Carlo (MC)simulation is utilized. The method is to sample the influencing factors, simulate the uncertain combination of various influencing factors, input the trained neural network prediction model as input, construct the MC-ANN joint model, obtain a large number of sample data under the influencing factors, and use the data. The relationship between the influencing factors of mining technology extraction and the neutral point DC is studied. Finally, the validity of test data of ±800kV Shanghai Temple-Shandong Linyi UHV transmission project is evaluated. The results demonstrate that the method can quickly find the test period. Site where the point of change has changed. This method can provide a theoretical reference for screening abnormal monitoring data, quickly finding the cause of the anomaly, and locking the grounding change site.