diff --git a/paddle/phi/api/yaml/sparse_api.yaml b/paddle/phi/api/yaml/sparse_api.yaml
index 904b1854489183ab9bf5a796ac6c1d9ef894ef19..e11306f21f24ec9f2feec28be3238acd729454eb 100644
--- a/paddle/phi/api/yaml/sparse_api.yaml
+++ b/paddle/phi/api/yaml/sparse_api.yaml
@@ -80,7 +80,7 @@
     data_type : x
   backward : cast_grad
 
-- api : conv3d_coo
+- api : conv3d
   args : (Tensor x, Tensor kernel, int[] paddings, int[] dilations, int[] strides, int groups, bool subm, str key)
   output : Tensor(out), Tensor(rulebook), Tensor(counter) 
   kernel :
diff --git a/paddle/phi/tests/api/test_sparse_conv_api.cc b/paddle/phi/tests/api/test_sparse_conv_api.cc
index b1df197f42f472b12c9891638f82a2a94569c57d..123f824f62d5157700e3c0e894e9c0f06a62bc31 100644
--- a/paddle/phi/tests/api/test_sparse_conv_api.cc
+++ b/paddle/phi/tests/api/test_sparse_conv_api.cc
@@ -76,7 +76,7 @@ void TestConv3dBase(const std::vector<int>& indices,
          kernel.size() * sizeof(T));
 
   if (!std::is_same<T, phi::dtype::float16>::value) {
-    auto tensor_out = paddle::experimental::sparse::conv3d_coo(
+    auto tensor_out = paddle::experimental::sparse::conv3d(
         x, weight, paddings, dilations, strides, 1, false, "Conv3d");
 
     auto out =
diff --git a/python/paddle/incubate/sparse/nn/functional/conv.py b/python/paddle/incubate/sparse/nn/functional/conv.py
index 60cbb94bea23642c1a20bc94e02f6ffa68a797a6..605cadc2b091e63d1fcc7ba1261797c152f00438 100644
--- a/python/paddle/incubate/sparse/nn/functional/conv.py
+++ b/python/paddle/incubate/sparse/nn/functional/conv.py
@@ -63,9 +63,9 @@ def _conv3d(x,
     dilation = convert_to_list(dilation, dims, 'dilation')
     op_type = "conv3d"
 
-    pre_bias = _C_ops.final_state_sparse_conv3d_coo(
-        x, weight, padding, dilation, stride, groups, subm,
-        key if key is not None else "")
+    pre_bias = _C_ops.final_state_sparse_conv3d(x, weight, padding, dilation,
+                                                stride, groups, subm,
+                                                key if key is not None else "")
     if bias is not None:
         values = pre_bias.values()
         add_bias = elementwise_add(values, bias, axis=1)