当前的rdd形式为：

Row(features=DenseVector([1.02, 4.23, 4.534, 0.342]))

根据official documentation中的示例，如果我们以以下形式获取它，它将工作：

[DenseVector([1.02, 4.23, 4.534, 0.342])]

将行矩阵构造为：

RowMatrix(df.rdd.map(list))

下面是一个完整的示例，它报告并修复了您的问题：

df = spark.createDataFrame(data=[([1.02, 4.23, 4.534, 0.342],)], schema=["features"])

from pyspark.sql.functions import udf
from pyspark.ml.linalg import VectorUDT
@udf(returnType=VectorUDT())
def arrayToVector(arrCol):
  from pyspark.ml.linalg import Vectors
  return Vectors.dense(arrCol)
# 

df = df.withColumn("features", arrayToVector("features"))
# print(df.head())
# df.printSchema()

# mat = RowMatrix(df.rdd) # Causes TypeError: Cannot convert type <class 'pyspark.sql.types.Row'> into Vector
mat = RowMatrix(df.rdd.map(list))
sims = mat.columnSimilarities()
print(sims.entries.collect())

[Out]:
[MatrixEntry(2, 3, 1.0), MatrixEntry(0, 1, 1.0), MatrixEntry(1, 2, 1.0), MatrixEntry(0, 3, 1.0), MatrixEntry(1, 3, 1.0), MatrixEntry(0, 2, 1.0)]