diff --git a/model_zoo/official/nlp/textcnn/src/textcnn.py b/model_zoo/official/nlp/textcnn/src/textcnn.py
index 88b3d7a907..3e16da594d 100644
--- a/model_zoo/official/nlp/textcnn/src/textcnn.py
+++ b/model_zoo/official/nlp/textcnn/src/textcnn.py
@@ -14,7 +14,6 @@
 # ============================================================================
 """TextCNN"""
 
-import numpy as np
 import mindspore.nn as nn
 import mindspore.ops.operations as P
 from mindspore import Tensor
@@ -89,17 +88,9 @@ class SoftmaxCrossEntropyExpand(Cell):
 
         return loss
 
-
-def _weight_variable(shape, factor=0.01):
-    init_value = np.random.randn(*shape).astype(np.float32) * factor
-    return Tensor(init_value)
-
-
 def make_conv_layer(kernel_size):
-    weight_shape = (96, 1, *kernel_size)
-    weight = _weight_variable(weight_shape)
     return nn.Conv2d(in_channels=1, out_channels=96, kernel_size=kernel_size, padding=1,
-                     pad_mode="pad", weight_init=weight, has_bias=True)
+                     pad_mode="pad", weight_init='uniform', has_bias=True)
 
 
 class TextCNN(nn.Cell):
@@ -113,7 +104,7 @@ class TextCNN(nn.Cell):
         self.num_classes = num_classes
 
         self.unsqueeze = P.ExpandDims()
-        self.embedding = nn.Embedding(vocab_len, self.vec_length, embedding_table='normal')
+        self.embedding = nn.Embedding(vocab_len, self.vec_length, embedding_table='uniform')
 
         self.slice = P.Slice()
         self.layer1 = self.make_layer(kernel_height=3)
@@ -125,7 +116,7 @@ class TextCNN(nn.Cell):
         self.fc = nn.Dense(96*3, self.num_classes)
         self.drop = nn.Dropout(keep_prob=0.5)
         self.print = P.Print()
-        self.reducemean = P.ReduceMax(keep_dims=False)
+        self.reducemax = P.ReduceMax(keep_dims=False)
 
     def make_layer(self, kernel_height):
         return nn.SequentialCell(
@@ -145,9 +136,9 @@ class TextCNN(nn.Cell):
         x2 = self.layer2(x)
         x3 = self.layer3(x)
 
-        x1 = self.reducemean(x1, (2, 3))
-        x2 = self.reducemean(x2, (2, 3))
-        x3 = self.reducemean(x3, (2, 3))
+        x1 = self.reducemax(x1, (2, 3))
+        x2 = self.reducemax(x2, (2, 3))
+        x3 = self.reducemax(x3, (2, 3))
 
         x = self.concat((x1, x2, x3))
         x = self.drop(x)