The test of analog-to-digital converter (ADC) mainly includes two test processes of static parameters and dynamic parameters. With the improvement in the performance, the testing complexity and cost of ADC increases dramatically. Alternative test, which means obtaining two types of parameters from only one test process by analyzing the relationship between static and dynamic parameters, has been proven to be a major solution to reducing the complexity and cost of ADC test. In this article, the alternative testing is achieved by constructing a regression model based on artificial neural network. The model takes total Harmonic distortion (THD) as the prediction target, and takes the static performance parameters as the input features. For high-dimensional ADC nonlinear curves, statistical analysis and principal component analysis are combined to design a special feature extraction method, which greatly reduces the feature dimension and the loss of information. The prediction results on the test set show that the mean absolute error and R-squared between the predicted THD and the reference value reach 1. 15 dB and 0. 6, respectively, which are significantly better than those of other comparison models. In addition, SHAP (shapley additive explanations) model interpreter is used to analyze the dependencies between the prediction target and feature variables of the model, and meaningful results are obtained.