diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index 5b079fce3a83d06d39f5997402666f82fab45925..7e6c3679704c5d97d55bd4d7c1167a56746f13ef 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -540,14 +540,16 @@ class Word2VecModel private[spark] (
     val cosineVec = Array.fill[Float](numWords)(0)
     val alpha: Float = 1
     val beta: Float = 0
-
+    // Normalize input vector before blas.sgemv to avoid Inf value
+    val vecNorm = blas.snrm2(vectorSize, fVector, 1)
+    if (vecNorm != 0.0f) {
+      blas.sscal(vectorSize, 1 / vecNorm, fVector, 0, 1)
+    }
     blas.sgemv(
       "T", vectorSize, numWords, alpha, wordVectors, vectorSize, fVector, 1, beta, cosineVec, 1)
 
-    // Need not divide with the norm of the given vector since it is constant.
     val cosVec = cosineVec.map(_.toDouble)
     var ind = 0
-    val vecNorm = blas.snrm2(vectorSize, fVector, 1)
     while (ind < numWords) {
       val norm = wordVecNorms(ind)
       if (norm == 0.0) {
@@ -557,17 +559,13 @@ class Word2VecModel private[spark] (
       }
       ind += 1
     }
-    var topResults = wordList.zip(cosVec)
+
+    wordList.zip(cosVec)
       .toSeq
       .sortBy(-_._2)
       .take(num + 1)
       .tail
-    if (vecNorm != 0.0f) {
-      topResults = topResults.map { case (word, cosVal) =>
-        (word, cosVal / vecNorm)
-      }
-    }
-    topResults.toArray
+      .toArray
   }
 
   /**
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
index 4fcf417d5f82efcf5452fd42e1bdbac55c378ca0..6d699440f2f2e06c595160b7382e723c6e3ae7c6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
@@ -108,5 +108,26 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("test similarity for word vectors with large values is not Infinity or NaN") {
+    val vecA = Array(-4.331467827487745E21, -5.26707742075006E21,
+      5.63551690626524E21, 2.833692188614257E21, -1.9688159903619345E21, -4.933950659913092E21,
+      -2.7401535502536787E21, -1.418671793782632E20).map(_.toFloat)
+    val vecB = Array(-3.9850175451103232E16, -3.4829783883841536E16,
+      9.421469251534848E15, 4.4069684466679808E16, 7.20936298872832E15, -4.2883302830374912E16,
+      -3.605579947835392E16, -2.8151294422155264E16).map(_.toFloat)
+    val vecC = Array(-1.9227381025734656E16, -3.907009342603264E16,
+      2.110207626838016E15, -4.8770066610651136E16, -1.9734964555743232E16, -3.2206001247617024E16,
+      2.7725358220443648E16, 3.1618718156980224E16).map(_.toFloat)
+    val wordMapIn = Map(
+      ("A", vecA),
+      ("B", vecB),
+      ("C", vecC)
+    )
+
+    val model = new Word2VecModel(wordMapIn)
+    model.findSynonyms("A", 5).foreach { pair =>
+      assert(!(pair._2.isInfinite || pair._2.isNaN))
+    }
+  }
 
 }
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 610d167f3ad08b8198cd56ba555305b797836067..1b059a719913dd26167a103d0ca8978fd3c9fd06 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -2186,13 +2186,14 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
     |   c|[-0.3794820010662...|
     +----+--------------------+
     ...
-    >>> model.findSynonyms("a", 2).show()
-    +----+-------------------+
-    |word|         similarity|
-    +----+-------------------+
-    |   b| 0.2505344027513247|
-    |   c|-0.6980510075367647|
-    +----+-------------------+
+    >>> from pyspark.sql.functions import format_number as fmt
+    >>> model.findSynonyms("a", 2).select("word", fmt("similarity", 5).alias("similarity")).show()
+    +----+----------+
+    |word|similarity|
+    +----+----------+
+    |   b|   0.25053|
+    |   c|  -0.69805|
+    +----+----------+
     ...
     >>> model.transform(doc).head().model
     DenseVector([0.5524, -0.4995, -0.3599, 0.0241, 0.3461])