diff --git a/PaddleRec/ssr/infer.py b/PaddleRec/ssr/infer.py
index 4dec0c0d0a09709c76da237445372f3d982e8f8c..09dee039f4da1e08de0169b3370aff174c89556b 100644
--- a/PaddleRec/ssr/infer.py
+++ b/PaddleRec/ssr/infer.py
@@ -95,7 +95,7 @@ def infer(args, vocab_size, test_reader):
                     user_data, pos_label = utils.infer_data(data, place)
                     all_item_numpy = np.tile(
                         np.arange(vocab_size), len(pos_label)).reshape(
-                            len(pos_label), vocab_size, 1)
+                            len(pos_label), vocab_size, 1).astype("int64")
                     para = exe.run(copy_program,
                                    feed={
                                        "user": user_data,
diff --git a/PaddleRec/word2vec/infer.py b/PaddleRec/word2vec/infer.py
index 26a1f2215e96d3c8ee325040f4c76ff93b8a6656..1b3290029d620d130d2fe7b7c2bcfd8bbeae54c2 100644
--- a/PaddleRec/word2vec/infer.py
+++ b/PaddleRec/word2vec/infer.py
@@ -88,17 +88,17 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w):
 
                     label = [dat[3] for dat in data]
                     input_word = [dat[4] for dat in data]
-                    para = exe.run(
-                        copy_program,
-                        feed={
-                            "analogy_a": wa,
-                            "analogy_b": wb,
-                            "analogy_c": wc,
-                            "all_label":
-                            np.arange(vocab_size).reshape(vocab_size, 1),
-                        },
-                        fetch_list=[pred.name, values],
-                        return_numpy=False)
+                    para = exe.run(copy_program,
+                                   feed={
+                                       "analogy_a": wa,
+                                       "analogy_b": wb,
+                                       "analogy_c": wc,
+                                       "all_label":
+                                       np.arange(vocab_size).reshape(
+                                           vocab_size, 1).astype("int64"),
+                                   },
+                                   fetch_list=[pred.name, values],
+                                   return_numpy=False)
                     pre = np.array(para[0])
                     val = np.array(para[1])
                     for ii in range(len(label)):