diff --git a/mindspore/nn/probability/toolbox/uncertainty_evaluation.py b/mindspore/nn/probability/toolbox/uncertainty_evaluation.py
index 1491a172c3..efdae0d9bd 100644
--- a/mindspore/nn/probability/toolbox/uncertainty_evaluation.py
+++ b/mindspore/nn/probability/toolbox/uncertainty_evaluation.py
@@ -47,6 +47,7 @@ class UncertaintyEvaluation:
                       Default: None.
         epochs (int): Total number of iterations on the data. Default: 1.
         epi_uncer_model_path (str): The save or read path of the epistemic uncertainty model. Default: None.
+                        If the epi_uncer_model_path is 'Untrain', the epistemic model need not to be trained.
         ale_uncer_model_path (str): The save or read path of the aleatoric uncertainty model. Default: None.
         save_model (bool): Whether to save the uncertainty model or not, if true, the epi_uncer_model_path
                         and ale_uncer_model_path must not be None. If false, the model to evaluate will be loaded from
@@ -109,7 +110,7 @@ class UncertaintyEvaluation:
         """
         if self.epi_uncer_model is None:
             self.epi_uncer_model = EpistemicUncertaintyModel(self.epi_model)
-            if self.epi_uncer_model.drop_count == 0:
+            if self.epi_uncer_model.drop_count == 0 and self.epi_uncer_model_path != 'Untrain':
                 if self.task_type == 'classification':
                     net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
                     net_opt = Adam(self.epi_uncer_model.trainable_params())
@@ -238,7 +239,7 @@ class EpistemicUncertaintyModel(Cell):
         for (name, layer) in epi_model.name_cells().items():
             if isinstance(layer, Dropout):
                 self.drop_count += 1
-            return epi_model
+                return epi_model
         for (name, layer) in epi_model.name_cells().items():
             if isinstance(layer, (Conv2d, Dense)):
                 uncertainty_layer = layer
@@ -246,7 +247,7 @@ class EpistemicUncertaintyModel(Cell):
                 drop = Dropout(keep_prob=dropout_rate)
                 bnn_drop = SequentialCell([uncertainty_layer, drop])
                 setattr(epi_model, uncertainty_name, bnn_drop)
-            return epi_model
+                return epi_model
         raise ValueError("The model has not Dense Layer or Convolution Layer, "
                          "it can not evaluate epistemic uncertainty so far.")