diff --git a/model_zoo/official/gnn/bgcf/src/metrics.py b/model_zoo/official/gnn/bgcf/src/metrics.py
index c7692604b5c18cfaa7a15bbaae4e9feeadf141f1..6c0024ab0be6556c91c5fac102f00bc371ebf96a 100644
--- a/model_zoo/official/gnn/bgcf/src/metrics.py
+++ b/model_zoo/official/gnn/bgcf/src/metrics.py
@@ -50,7 +50,7 @@ def novelty_at_k(topk_items, item_degree_dict, num_user, k):
     """Calculate the novelty at k"""
     avg_nov = []
     for item in topk_items[:k]:
-        avg_nov.append(-np.log2(item_degree_dict[item] / num_user))
+        avg_nov.append(-np.log2((item_degree_dict[item] + 1e-8) / num_user))
     return np.mean(avg_nov)
 
 
diff --git a/model_zoo/official/gnn/bgcf/train.py b/model_zoo/official/gnn/bgcf/train.py
index 148fbe0cc2546b3403a5f7623a60f9ac22919f40..055aebb96844521001d80b4d507a7d3d6b53ff6b 100644
--- a/model_zoo/official/gnn/bgcf/train.py
+++ b/model_zoo/official/gnn/bgcf/train.py
@@ -16,6 +16,7 @@
 BGCF training script.
 """
 import os
+import time
 import datetime
 
 from mindspore import Tensor
@@ -56,6 +57,7 @@ def train_and_eval():
 
     for _epoch in range(1, parser.num_epoch + 1):
 
+        epoch_start = time.time()
         iter_num = 1
 
         for data in itr:
@@ -96,7 +98,7 @@ def train_and_eval():
             if iter_num == num_iter:
                 print('Epoch', '%03d' % _epoch, 'iter', '%02d' % iter_num,
                       'loss',
-                      '{}'.format(train_loss))
+                      '{}, cost:{:.4f}'.format(train_loss, time.time() - epoch_start))
             iter_num += 1
 
         if _epoch % parser.eval_interval == 0: