diff --git a/mindspore/ccsrc/minddata/dataset/engine/datasetops/bucket_batch_by_length_op.cc b/mindspore/ccsrc/minddata/dataset/engine/datasetops/bucket_batch_by_length_op.cc
index 9f4866f92e6..30da571089e 100644
--- a/mindspore/ccsrc/minddata/dataset/engine/datasetops/bucket_batch_by_length_op.cc
+++ b/mindspore/ccsrc/minddata/dataset/engine/datasetops/bucket_batch_by_length_op.cc
@@ -155,8 +155,17 @@ Status BucketBatchByLengthOp::ObtainElementLength(int32_t *out_element_length, T
   // call pyfunc here if given pyfunc, otherwise return 0th dimension of shape of
   // the single column specified in length_dependent_columns_
   if (element_length_function_) {
-    TensorRow output;
-    RETURN_IF_NOT_OK(element_length_function_->Compute(element, &output));
+    TensorRow input, output;
+    size_t number_of_arguments = length_dependent_columns_.size();
+    for (size_t i = 0; i < number_of_arguments; i++) {
+      auto map_item = column_name_id_map_.find(length_dependent_columns_[i]);
+      if (map_item == column_name_id_map_.end()) {
+        RETURN_STATUS_UNEXPECTED("BucketBatchByLength: Couldn't find the specified column in the dataset");
+      }
+      int32_t column_index = map_item->second;
+      input.push_back(element[column_index]);
+    }
+    RETURN_IF_NOT_OK(element_length_function_->Compute(input, &output));
     RETURN_IF_NOT_OK(output.at(0)->GetItemAt(out_element_length, {0}));
     if (*out_element_length < 0) {
       RETURN_STATUS_UNEXPECTED("BucketBatchByLength: element_length_function returned negative integer");
diff --git a/tests/ut/python/dataset/test_bucket_batch_by_length.py b/tests/ut/python/dataset/test_bucket_batch_by_length.py
index d0f102b8aef..bc5993fd212 100644
--- a/tests/ut/python/dataset/test_bucket_batch_by_length.py
+++ b/tests/ut/python/dataset/test_bucket_batch_by_length.py
@@ -36,6 +36,11 @@ def generate_2_columns(n):
         yield (np.array([i]), np.array([j for j in range(i + 1)]))
 
 
+def generate_3_columns(n):
+    for i in range(n):
+        yield (np.array([i]), np.array([i + 1]), np.array([j for j in range(i + 1)]))
+
+
 def test_bucket_batch_invalid_input():
     dataset = ds.GeneratorDataset((lambda: generate_sequential_same_shape(10)), ["col1"])
 
@@ -382,6 +387,48 @@ def test_bucket_batch_multi_column():
     assert same_shape_output == same_shape_expected_output
     assert variable_shape_output == variable_shape_expected_output
 
+
+def test_bucket_batch_three_columns():
+    dataset = ds.GeneratorDataset((lambda: generate_3_columns(10)), ["same_shape", "same_shape2", "variable_shape"])
+
+    column_names = ["same_shape2"]
+    bucket_boundaries = [6, 12]
+    bucket_batch_sizes = [5, 5, 1]
+    element_length_function = (lambda x: x[0] % 3)
+    pad_info = {}
+
+    dataset = dataset.bucket_batch_by_length(column_names, bucket_boundaries,
+                                             bucket_batch_sizes, element_length_function,
+                                             pad_info)
+
+    same_shape_expected_output = [[[0], [1], [2], [3], [4]],
+                                  [[5], [6], [7], [8], [9]]]
+    same_shape2_expected_output = [[[1], [2], [3], [4], [5]],
+                                   [[6], [7], [8], [9], [10]]]
+    variable_shape_expected_output = [[[0, 0, 0, 0, 0],
+                                       [0, 1, 0, 0, 0],
+                                       [0, 1, 2, 0, 0],
+                                       [0, 1, 2, 3, 0],
+                                       [0, 1, 2, 3, 4]],
+                                      [[0, 1, 2, 3, 4, 5, 0, 0, 0, 0],
+                                       [0, 1, 2, 3, 4, 5, 6, 0, 0, 0],
+                                       [0, 1, 2, 3, 4, 5, 6, 7, 0, 0],
+                                       [0, 1, 2, 3, 4, 5, 6, 7, 8, 0],
+                                       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]]
+
+    same_shape_output = []
+    same_shape2_output = []
+    variable_shape_output = []
+    for data in dataset.create_dict_iterator(num_epochs=1):
+        same_shape_output.append(data["same_shape"].tolist())
+        same_shape2_output.append(data["same_shape2"].tolist())
+        variable_shape_output.append(data["variable_shape"].tolist())
+
+    assert same_shape_output == same_shape_expected_output
+    assert same_shape2_output == same_shape2_expected_output
+    assert variable_shape_output == variable_shape_expected_output
+
+
 def test_bucket_batch_get_dataset_size():
     dataset = ds.GeneratorDataset((lambda: generate_sequential_same_shape(10)), ["col1"])
 
@@ -402,6 +449,25 @@ def test_bucket_batch_get_dataset_size():
     assert data_size == num_rows
 
 
+def test_bucket_batch_invalid_column():
+    dataset = ds.GeneratorDataset((lambda: generate_sequential_same_shape(10)), ["col1"])
+
+    column_names = ["invalid_column"]
+    bucket_boundaries = [1, 2, 3]
+    bucket_batch_sizes = [3, 3, 2, 2]
+    element_length_function = (lambda x: x[0] % 4)
+
+    dataset = dataset.bucket_batch_by_length(column_names, bucket_boundaries,
+                                             bucket_batch_sizes, element_length_function)
+
+    with pytest.raises(RuntimeError) as info:
+        num_rows = 0
+        for _ in dataset.create_dict_iterator(num_epochs=1):
+            num_rows += 1
+
+        assert "BucketBatchByLength: Couldn't find the specified column in the dataset" in str(info.value)
+
+
 if __name__ == '__main__':
     test_bucket_batch_invalid_input()
     test_bucket_batch_multi_bucket_no_padding()
@@ -413,4 +479,6 @@ if __name__ == '__main__':
     test_bucket_batch_drop_remainder()
     test_bucket_batch_default_length_function()
     test_bucket_batch_multi_column()
+    test_bucket_batch_three_columns()
     test_bucket_batch_get_dataset_size()
+    test_bucket_batch_invalid_column()