diff --git "a/\347\254\2543\347\253\240_\347\261\273\345\210\253\346\257\224\350\276\203\345\236\213\345\233\276\350\241\250/\345\233\2763-9-3 \345\215\225\347\257\207\346\226\207\347\253\240\347\232\204\350\257\215\344\272\221\345\233\276.R" "b/\347\254\2543\347\253\240_\347\261\273\345\210\253\346\257\224\350\276\203\345\236\213\345\233\276\350\241\250/\345\233\2763-9-3 \345\215\225\347\257\207\346\226\207\347\253\240\347\232\204\350\257\215\344\272\221\345\233\276.R"
index 90d9b4e..f3b38bc 100644
--- "a/\347\254\2543\347\253\240_\347\261\273\345\210\253\346\257\224\350\276\203\345\236\213\345\233\276\350\241\250/\345\233\2763-9-3 \345\215\225\347\257\207\346\226\207\347\253\240\347\232\204\350\257\215\344\272\221\345\233\276.R"	
+++ "b/\347\254\2543\347\253\240_\347\261\273\345\210\253\346\257\224\350\276\203\345\236\213\345\233\276\350\241\250/\345\233\2763-9-3 \345\215\225\347\257\207\346\226\207\347\253\240\347\232\204\350\257\215\344\272\221\345\233\276.R"	
@@ -1,41 +1,47 @@
-#EasyCharts团队出品，
+
+#EasyShu团队出品，更多文章请关注微信公众号【EasyShu】
 #如有问题修正与深入学习，可联系微信：EasyCharts
 
 library(tm)
 library(wordcloud)
 
-Paper1<-paste(scan("Paper1.txt", what = character(0),sep = ""), collapse = " ")  
-Paper2<-paste(scan("Paper2.txt", what = character(0),sep = ""), collapse = " ")  
-
+Paper1<-paste(scan("Paper1.txt", what = character(0),sep = ""), collapse = " ") #读入TXT 文档1
+Paper2<-paste(scan("Paper2.txt", what = character(0),sep = ""), collapse = " ") #读入TXT 文档2
 tmpText<- data.frame(c(Paper1, Paper2),row.names=c("Text1","Text2"))
-
 df_title <- data.frame(doc_id=row.names(tmpText),
                        text=tmpText$c.Paper1..Paper2.)
-
 ds <- DataframeSource(df_title)
-corp = Corpus(ds)
-corp = tm_map(corp,removePunctuation)
-corp = tm_map(corp,PlainTextDocument)
-corp = tm_map(corp,removeNumbers)
-corp = tm_map(corp, function(x){removeWords(x,stopwords())})
-
+#创建一个数据框格式的数据源，首列是文档id(doc_id),第二列是文档内容
+corp <- VCorpus(ds)
+#加载文档集中的文本并生成语料库文件
+corp<- tm_map(corp,removePunctuation) #清除语料库内的标点符号
+corp <- tm_map(corp,PlainTextDocument) #转换为纯文本
+corp <- tm_map(corp,removeNumbers) #清除数字符号
+corp <- tm_map(corp, function(x){removeWords(x,stopwords())}) #过滤停止词库
 term.matrix <- TermDocumentMatrix(corp)
-term.matrix <- as.matrix(term.matrix)
+#利用TermDocumentMatrix()函数将处理后的语料库进行断字处理，生成词频权重矩阵
+
+term.matrix <- as.matrix(term.matrix) #频率
 colnames(term.matrix) <- c("Paper1","paper2")
+df<-data.frame(term.matrix)
+write.csv(df,'term_matrix.csv') #导出两篇文章的频率分析结果
+
+#---------------------------------------导入数据------------------------------------------
+df<-read.csv('term_matrix.csv',header=TRUE,row.names=1)
 
-#------------------------------------------------------------------------------------------------------
-comparison.cloud(term.matrix, max.words=300, random.order=FALSE, rot.per=.15, c(4,0.4), title.size=1.4)
+#-------------------------------------单篇文章数据的展示-----------------------------------------------------------------
+#Colors<-colorRampPalette(rev(brewer.pal(9,'RdBu')))(length(df$Paper1>10))
+wordcloud(row.names(df) , df$Paper1 , min.freq=10,col=brewer.pal(8, "Dark2"), rot.per=0.3 )
+
+
+#----------------------------------------两篇文章数据的对比-------------------------------------------------------------
+comparison.cloud(df, max.words=300, random.order=FALSE, rot.per=.15, c(4,0.4), title.size=1.4)
 
-comparison.cloud(term.matrix,max.words=300,random.order=FALSE,colors=c("#00B2FF", "red"))
-commonality.cloud(term.matrix,max.words=100,random.order=FALSE,color="#E7298A")
+comparison.cloud(df,max.words=300,random.order=FALSE,colors=c("#00B2FF", "red"))
+commonality.cloud(df,max.words=100,random.order=FALSE,color="#E7298A")
 
 
 # comparison cloud
-comparison.cloud(term.matrix, random.order=FALSE, 
+comparison.cloud(df, random.order=FALSE, 
                  colors = c("#00B2FF", "red", "#FF0099", "#6600CC"),
                  title.size=1.5, max.words=500)
-
-#------------------------------------------------------------------------------------------------------
-df<-data.frame(term.matrix)
-#Colors<-colorRampPalette(rev(brewer.pal(9,'RdBu')))(length(df$Paper1>10))
-wordcloud(row.names(df) , df$Paper1 , min.freq=10,col=brewer.pal(8, "Dark2"), rot.per=0.3 )