diff --git a/spark-lesson/src/main/scala/date_20250415/RDDAverageScore.scala b/spark-lesson/src/main/scala/date_20250415/RDDAverageScore.scala
index 8a09672..e0682ed 100755
--- a/spark-lesson/src/main/scala/date_20250415/RDDAverageScore.scala
+++ b/spark-lesson/src/main/scala/date_20250415/RDDAverageScore.scala
@@ -17,9 +17,9 @@ object RDDAverageScore {
     val sc = new SparkContext(conf)
 
     // 读取每个科目的成绩文件
-    val mathRDD: RDD[String] = sc.textFile("hdfs://192.168.182.100:9000/data/math.txt")
-    val chineseRDD: RDD[String] = sc.textFile("hdfs://192.168.182.100:9000/data/chinese.txt")
-    val englishRDD: RDD[String] = sc.textFile("hdfs://192.168.182.100:9000/data/english.txt")
+    val mathRDD: RDD[String] = sc.textFile("hdfs://hadoop102:8020/input/data/math.txt")
+    val chineseRDD: RDD[String] = sc.textFile("hdfs://hadoop102:8020/input/data/chinese.txt")
+    val englishRDD: RDD[String] = sc.textFile("hdfs://hadoop102:8020/input/data/english.txt")
 
     // 处理每个科目RDD，将每一行数据转化为 (姓名, 分数) 二元组
     val mathScores: RDD[(String, Int)] = mathRDD.map(line => {
@@ -41,7 +41,8 @@ object RDDAverageScore {
     val allScores: RDD[(String, Int)] = mathScores.union(chineseScores).union(englishScores)
 
     // 对每个学生的所有分数进行聚合 (姓名, (总分, 科目数))
-    val studentScores: RDD[(String, (Int, Int))] = allScores.补充
+    val studentScores: RDD[(String, (Int, Int))] = allScores
+      .map( x => (x._1,(x._2,1)))
       .reduceByKey { case ((sum1, count1), (sum2, count2)) =>
         (sum1 + sum2, count1 + count2)
       }