Objective：To explore the correlation between pathological indicators of renal allograft biopsy and abnormal renal allograft function at different stages，to evaluate the diagnostic value of pathological indicators in abnormal renal allograft function，and to establish a model for predicting the prognosis of renal allografts. Methods：This study retrospectively analyzed the clinical data and pathological indicators of kidney transplant recipients who underwent renal allograft biopsy at the First Affiliated Hospital of Nanjing Medical University between 2015 and 2018. The total sample was statistically analyzed based on different post-biopsy times（at biopsy， 1 year after biopsy，3 years after biopsy，and 5 years after biopsy）. Univariate analysis was conducted to screen for indicators that affect abnormal renal allograft function. The model was established using binary logistic regression to draw a nomogram. Mixed - effects logistic regression was further applied to investigate the dynamic effects of factors consistently associated with abnormal renal allograft function across all stages. According to receiver operating characteristic curve（ROC），corresponding Area under the curve（AUC）and calibration curve，the discriminant validity and consistency of the model with the actual situation were judged. Results：A total of 121 patients were included in this study. According to the time after biopsy of the transplanted kidney，the total samples were divided into four groups：at the time of biopsy，1 year after biopsy，3 years after biopsy，and 5 years after biopsy. The i score ≥1 was significantly correlated with renal function at the time of biopsy and at 1，3，and 5 years after biopsy. The dynamic effects analysis indicated that i-score ≥ 1 was associated with renal function deterioration within five years after kidney transplantation. A nomogram was constructed based on the above statistical results. The AUC indicated that the model had good discriminative validity. The calibration curve showed that the predicted probability of abnormal renal allograft function was in good agreement with the actual probability. Conclusion：This study constructed a nomogram model capable of predicting abnormal renal allograft function at different time points which demonstrated a good predictive ability and could effectively improve the precision of post - biopsy patient management for renal transplant recipients.