diff --git a/mindspore/numpy/array_creations.py b/mindspore/numpy/array_creations.py
index 90a2b7f8ab0..25e997deb34 100644
--- a/mindspore/numpy/array_creations.py
+++ b/mindspore/numpy/array_creations.py
@@ -83,6 +83,8 @@ def array(obj, dtype=None, copy=True, ndmin=0):
         >>> print(np.array([1,2,3]))
         [1 2 3]
     """
+    if dtype is not None:
+        dtype = _check_dtype(dtype)
     res = asarray(obj, dtype)
 
     if ndmin > res.ndim:
@@ -163,6 +165,8 @@ def asarray(a, dtype=None):
         >>> print(np.asarray([1,2,3]))
         [1 2 3]
     """
+    if dtype is not None:
+        dtype = _check_dtype(dtype)
     if isinstance(a, Tensor):
         if dtype is None or dtype == a.dtype:
             return a
diff --git a/mindspore/numpy/logic_ops.py b/mindspore/numpy/logic_ops.py
index bc12a39ecb2..8d00660737d 100644
--- a/mindspore/numpy/logic_ops.py
+++ b/mindspore/numpy/logic_ops.py
@@ -747,7 +747,7 @@ def array_equal(a1, a2, equal_nan=False):
     Returns `True` if input arrays have same shapes and all elements equal.
 
     Note:
-        In mindpsore, a bool tensor is returned instead, since in Graph mode, the
+        In mindspore, a bool tensor is returned instead, since in Graph mode, the
         value cannot be traced and computed at compile time.
 
         Since on Ascend, :class:`nan` is treated differently, currently the argument
@@ -793,7 +793,7 @@ def array_equiv(a1, a2):
     be broadcasted to create the same shape as the other one.
 
     Note:
-        In mindpsore, a bool tensor is returned instead, since in Graph mode, the
+        In mindspore, a bool tensor is returned instead, since in Graph mode, the
         value cannot be traced and computed at compile time.
 
     Args: