diff --git a/fluid/PaddleRec/word2vec/infer.py b/fluid/PaddleRec/word2vec/infer.py
index 5787561040d2aba2fd84fc4453eff4a8f00f7f3f..9ed42d1c62790980d832fa0251d80a01b2986417 100644
--- a/fluid/PaddleRec/word2vec/infer.py
+++ b/fluid/PaddleRec/word2vec/infer.py
@@ -3,6 +3,7 @@ import os
 import paddle.fluid as fluid
 import numpy as np
 from Queue import PriorityQueue
+import heapq
 import logging
 import argparse
 import preprocess
@@ -196,27 +197,12 @@ def topK(k, cosine_similarity_list, exclude_list, is_acc=False):
                 pass
         return [[max, id]]
     else:
-        pq = PriorityQueue(k + 1)
-        while not pq.empty():
-            try:
-                pq.get(False)
-            except Empty:
-                continue
-            pq.task_done()
-        if len(cosine_similarity_list) <= k:
-            for i in range(len(cosine_similarity_list)):
-                pq.put([cosine_similarity_list[i], i])
-            return pq
-
-        for i in range(len(cosine_similarity_list)):
-            if is_acc and (i in exclude_list):
-                pass
-            else:
-                if pq.full():
-                    pq.get()
-                pq.put([cosine_similarity_list[i], i])
-        pq.get()
-        return pq
+        result = list()
+        result_index = np.argpartition(cosine_similarity_list, -k)[-k:]
+        for index in result_index:
+            result.append([cosine_similarity_list[index], index])
+        result.sort(reverse=True)
+        return result
 
 
 def topKs(k, cosine_similarity_matrix, exclude_lists, is_acc=False):
@@ -233,16 +219,7 @@ def topKs(k, cosine_similarity_matrix, exclude_lists, is_acc=False):
             tmp_pq = topK(k, cosine_similarity_matrix[i], exclude_lists[0],
                           is_acc)
         result_queues.append(tmp_pq)
-    if is_acc and k == 1:  # accelerate acc calculate
-        return result_queues
-    else:
-        for pq in result_queues:
-            tmp_result = list()
-            for i in range(k):
-                tmp_result.append(pq.get())
-            tmp_result.reverse()
-            results.append(tmp_result)
-        return results
+    return result_queues
 
 
 def infer_during_train(args):