diff --git a/mindspore/python/mindspore/nn/optim/thor.py b/mindspore/python/mindspore/nn/optim/thor.py
index 33cbdf591c0..da35f298e86 100644
--- a/mindspore/python/mindspore/nn/optim/thor.py
+++ b/mindspore/python/mindspore/nn/optim/thor.py
@@ -256,8 +256,7 @@ def thor(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0
 
     Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation (THOR) algorithm is proposed in:
 
-    `THOR: Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation
-    <https://www.aaai.org/AAAI21Papers/AAAI-6611.ChenM.pdf>`_
+    `THOR: Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation`_
 
     The updating formulas are as follows,
 
@@ -973,15 +972,15 @@ class ThorAscend(Optimizer):
             matrix_g_combine_shape = self.shape(matrix_g_inv)
             if matrix_a_inv_shape[0] == 2048 and matrix_g_combine_shape[0] == 1001:
                 matrix_a_inv = self.reshape(matrix_a_inv,
-                                            (matrix_a_inv_shape[0] / 16, 16,
-                                             matrix_a_inv_shape[0] / 16, 16))
+                                            (matrix_a_inv_shape[0] // 16, 16,
+                                             matrix_a_inv_shape[0] // 16, 16))
                 matrix_a_inv = self.transpose(matrix_a_inv, (2, 0, 1, 3))
                 matrix_g_inv = P.Pad(((0, 7), (0, 7)))(matrix_g_inv)
 
                 matrix_g_inv_shape = self.shape(matrix_g_inv)
                 matrix_g_inv = self.reshape(matrix_g_inv,
-                                            (matrix_g_inv_shape[0] / 16, 16,
-                                             matrix_g_inv_shape[0] / 16, 16))
+                                            (matrix_g_inv_shape[0] // 16, 16,
+                                             matrix_g_inv_shape[0] // 16, 16))
                 matrix_g_inv = self.transpose(matrix_g_inv, (2, 0, 1, 3))
 
         matrix_a_allreduce = matrix_a_allreduce + (matrix_a_inv,)