In order to solve the problem that it is difficult to explain the prediction results of just-in-time software defects, based on the improved model of polynomial neural network, an ante-hoc interpretable just-in-time software defect prediction method is proposed. This method formalized the causal relationship between code metric elements and prediction results, and outputted it as a K-G polynomial function. The standardized regression coefficient was used to measure the importance of metric elements to analyze the causes of the defects. The experimental results show that on the premise that the average prediction accuracy reaches 0.797, it has good interpretability at the same time.