思路：讀取源文件，形成分詞列表，然後讀取停用詞，將不必要的詞語進行去除，然後統計詞頻，詞云引數設定，繪製詞雲圖。
"""匯入相關庫"""
import jieba
import pandas as pd
from imageio import imread
from wordcloud import wordcloud
from matplotlib import pyplot as plt
"""讀取原始檔，然後形成分詞列表"""
with open('e:/yuanwenjian.txt','r',encoding='utf-8') as f:
txt = f.read()
txt = txt.split()
data_cut = [jieba.lcut(x) for x in txt] #分詞後結果，形式為二維列表（裡面是列表）
all_words = #轉化成一維列表（裡面是字串）
for i in data_cut:
all_words.extend(i)
# all_words.count('詞語') #統計詞頻
"""讀取停用詞文件"""
with open("e:\\stopwords.txt",'r',encoding='utf-8') as f:
stop=f.read()
stop = stop.split()
stop = [' ']+stop
data_after = [[j for j in i if j not in stop] for i in data_cut] #判斷是否為停用詞
"""統計詞頻"""
all_words =
for i in data_after:
all_words.extend(i)
num = pd.series(all_words).value_counts()
"""讀取背景"""
pic = imread('e:/素材/logo/python.png')
"""詞云引數"""
wc = wordcloud(background_color = 'white',font_path='c:\\windows\\fonts\\simkai.ttf',mask=pic)
'''wc = wordcloud(background_color = 'white',
font_path='c:\\windows\\fonts\\simkai.ttf',
max_words=200,
max_font_size=10,
mask=pic)
'''wc2 = wc.fit_words(num) #詞頻傳入
"""詞云展示"""
plt.figure(figsize=(9,9)) #的大小
plt.imshow(wc2)
plt.axis('off') #關閉座標
plt.show()
wc.to_file("ciyun.png") #儲存
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