diff --git a/mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_util.cc b/mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_util.cc
index cc5a11f8bb2..4f52e119f1f 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_util.cc
+++ b/mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_util.cc
@@ -37,6 +37,8 @@ static const std::map<int32_t, int32_t> kMsProtoDataTypeMap = {
   {mindspore::TypeId::kNumberTypeFloat, mindspore::DataType::MS_FLOAT32},
   {mindspore::TypeId::kNumberTypeFloat32, mindspore::DataType::MS_FLOAT32},
   {mindspore::TypeId::kNumberTypeFloat64, mindspore::DataType::MS_FLOAT64},
+  {mindspore::TypeId::kNumberTypeComplex64, mindspore::DataType::MS_COMPLEX64},
+  {mindspore::TypeId::kNumberTypeComplex128, mindspore::DataType::MS_COMPLEX128},
 };
 
 static const std::map<int32_t, int32_t> kProtoDataTypeToMsDataTypeMap = {
@@ -53,6 +55,8 @@ static const std::map<int32_t, int32_t> kProtoDataTypeToMsDataTypeMap = {
   {mindspore::DataType::MS_FLOAT16, mindspore::TypeId::kNumberTypeFloat16},
   {mindspore::DataType::MS_FLOAT32, mindspore::TypeId::kNumberTypeFloat32},
   {mindspore::DataType::MS_FLOAT64, mindspore::TypeId::kNumberTypeFloat64},
+  {mindspore::DataType::MS_COMPLEX64, mindspore::TypeId::kNumberTypeComplex64},
+  {mindspore::DataType::MS_COMPLEX128, mindspore::TypeId::kNumberTypeComplex128},
 };
 
 int AicpuOpUtil::MsTypeToProtoType(TypeId ms_type) {
diff --git a/mindspore/ccsrc/pybind_api/ir/tensor_py.cc b/mindspore/ccsrc/pybind_api/ir/tensor_py.cc
index 694df059fdd..a809e284d90 100644
--- a/mindspore/ccsrc/pybind_api/ir/tensor_py.cc
+++ b/mindspore/ccsrc/pybind_api/ir/tensor_py.cc
@@ -456,6 +456,16 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) {
                                   return TensorPy::MakeTensor(py::array(input), type_ptr);
                                 }),
                                 py::arg("input"), py::arg("dtype") = nullptr)
+                           // We only suppot array/bool_/int_/float_/list/tuple/complex pybind objects as tensor input,
+                           // and array/bool_/int_/float_/list/tuple init will be matched above, other pybind objects
+                           // input will raise error except complex data type.
+                           .def(py::init([](const py::object &input, const TypePtr &type_ptr) {
+                                  if (!PyComplex_CheckExact(input.ptr())) {
+                                    MS_LOG(EXCEPTION) << "Unsupported tensor type: " << input.get_type();
+                                  }
+                                  return TensorPy::MakeTensor(py::array(input), type_ptr);
+                                }),
+                                py::arg("input"), py::arg("dtype") = nullptr)
                            .def_property("init_flag", &Tensor::is_init, &Tensor::set_init_flag)
                            .def_property_readonly("_dtype", &Tensor::Dtype, R"mydelimiter(
                              Get the tensor's data type.
diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py
index fdca6e5cdf5..69d5caed8bb 100644
--- a/mindspore/common/tensor.py
+++ b/mindspore/common/tensor.py
@@ -89,8 +89,8 @@ class Tensor(Tensor_):
 
         # If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
         if init is None:
-            validator.check_value_type('input_data', input_data, (Tensor_, np.ndarray, list, tuple, float, int, bool),
-                                       'Tensor')
+            validator.check_value_type('input_data', input_data,
+                                       (Tensor_, np.ndarray, list, tuple, float, int, bool, complex), 'Tensor')
             valid_dtypes = (np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64,
                             np.float16, np.float32, np.float64, np.bool_, np.str_, np.complex64, np.complex128)
             if isinstance(input_data, np.ndarray) and input_data.dtype not in valid_dtypes and \
diff --git a/tests/ut/python/ir/test_tensor.py b/tests/ut/python/ir/test_tensor.py
index 6f5ef0233ac..9acb4254996 100644
--- a/tests/ut/python/ir/test_tensor.py
+++ b/tests/ut/python/ir/test_tensor.py
@@ -94,6 +94,14 @@ def test_tensor_type_complex64_user_define():
 
 
 def test_tensor_type_complex128():
+    #complex python object
+    py_input = 1 + 2.22222222j
+    t_complex128 = ms.Tensor(py_input)
+    assert t_complex128.shape == ()
+    assert t_complex128.dtype == ms.complex128
+    assert np.all(t_complex128.asnumpy() == py_input)
+
+    #complex in numpy array
     np_input = np.array(
         [[1+0.1j, 2j, 3+0.3j], [4-0.4j, 5, 6]], dtype=np.complex128)
     t_complex128 = ms.Tensor(np_input)
@@ -101,10 +109,19 @@ def test_tensor_type_complex128():
     assert t_complex128.shape == (2, 3)
     assert t_complex128.dtype == ms.complex128
     assert np.all(t_complex128.asnumpy() == np_input)
-    np_input = (1, 2.22222222j, 3)
-    t_complex128 = ms.Tensor(np_input)
-    assert np.all(t_complex128.asnumpy() == np_input)
 
+    #complex in tuple
+    py_input = (1, 2.22222222j, 3)
+    t_complex128 = ms.Tensor(py_input)
+    assert np.all(t_complex128.asnumpy() == py_input)
+
+    #complex in list
+    py_input = [[1+0.1j, 2j, 3+0.3j], [4-0.4j, 5, 6]]
+    t_complex128 = ms.Tensor(py_input)
+    assert isinstance(t_complex128, ms.Tensor)
+    assert t_complex128.shape == (2, 3)
+    assert t_complex128.dtype == ms.complex128
+    assert np.all(t_complex128.asnumpy() == py_input)
 
 def test_tensor_type_complex128_user_define():
     np_input = np.zeros([1, 2, 3])